Grupo 1 - MSF - Predicción de aumento de cuotas de socios

En este proyecto vamos a analizar los datos aportados por Medicos Sin Fronteras y preparar un modelo para la predicción de aumento de cuotas de socios.

Fase 0. Configurar entorno local¶

In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
In [2]:
pd.set_option('display.max_columns', 1200)
pd.set_option('display.max_rows', 1200)
pd.set_option('max_colwidt', None)

Fase 1: Carga de datos¶

Carga los datos de la tabla "Contactos"¶

In [3]:
# Importamos dataset de CONTACTOS
df_contactos = pd.read_parquet("MSF_Contact.parquet")
df_contactos.head()
Out[3]:
id msf_seniority__c npo02__best_gift_year__c msf_birthyear__c msf_entrycampaign__c msf_firstcampaignrecurringdonorchannel__c leadsource msf_firstcampaigncolaborationchannel__c msf_returnedmail__c npo02__averageamount__c msf_isactivedonor__c msf_isactiverecurringdonor__c npsp__deceased__c msf_begindatemsf__c msf_fechacambiolevelrelacion__c msf_datefirstdonation__c msf_datefirstrecurringdonorquota__c msf_datelastrecurringdonorquota__c msf_datelastdonation__c npsp__largest_soft_credit_date__c npsp__first_soft_credit_date__c msf_entrydatecurrentrecurringdonor__c npsp__last_soft_credit_date__c msf_firstentrydaterecurringdonor__c npo02__firstclosedate__c msf_lastrecurringdonationdate__c npo02__lastclosedate__c msf_lastdonationunique__c gender__c msf_crmexternalid__c msf_languagepreferer__c npo02__largestamount__c npo02__smallestamount__c npsp__first_soft_credit_amount__c npo02__lastoppamount__c npsp__last_soft_credit_amount__c msf_annualizedquotachange__c msf_relationshiplevel__c msf_ltvcont__c msf_ltvdesc__c msf_ltvscore__c mailingstate npsp__largest_soft_credit_amount__c msf_contactdeletereason__c npo02__soft_credit_last_year__c npo02__soft_credit_this_year__c npo02__soft_credit_two_years_ago__c msf_noagradecimientosmi__c msf_noagradecimientoscp__c msf_noagradecimientosemail__c msf_noagradecimientossms__c msf_noagradecimientostelefono__c msf_nocaptacionfondoscp__c msf_nocaptacionfondosemail__c msf_nocaptacionfondosmi__c msf_nocaptacionfondossms__c msf_nocartasplanrelacioncp__c msf_nocertificadofiscalcp__c msf_nocertificadofiscalemail__c msf_nocertificadofiscalmi__c msf_nocertificadofiscalsms__c msf_nocesionimagenpromocion__c msf_nocomunicacionesonetooneemail__c msf_nocomunicaconesonetoonemi__c msf_nocomunicacionesonetoonecp__c msf_nocomunicaconesonetoonesms__c msf_nocomunicacionesonetoonetelefono__c msf_noemailingstematicosemail__c msf_noencuestaestudioconcursoemail__c msf_noencuestaestudioconcursomi__c msf_nollamadasbienvenidasencuestasotras__c msf_noencuestaestudioconcursosms__c msf_noencuestaestudioconcursotelefono__c msf_noinformaciontestamentaria__c msf_noinvitacioneseventosmi__c msf_noinvitacioneseventoscp__c msf_noinvitacioneseventosemail__c msf_noinvitacioneseventossms__c msf_noinvitacioneseventostelefono__c msf_nomailingstematicoscp__c msf_nomemoriacp__c msf_nomemoriaemail__c msf_nomemoriami__c msf_nomemoriasms__c msf_nomensajesplanrelacionsms__c msf_nomensajestematicosmi__c msf_nomensajestematicossms__c msf_nonewsletteremail__c msf_noplanrelacionemail__c msf_nomensajesplanrelacionmi__c msf_noplanrelaciontelefono__c msf_norevistacp__c msf_norevistaemail__c msf_norevistami__c msf_norevistasms__c msf_notelemarketingcaptacionfondos__c msf_hasfirstdonation__c msf_hasfirstnewrecurringdonation__c msf_firstcampaignentryrecurringdonor__c msf_firstcampaingcolaboration__c msf_firstannualizedquota__c msf_program__c msf_programaherencias__c msf_programais__c msf_pressurecomplaint__c msf_recencydonorcont__c msf_recencydonordesc__c msf_recencyrecurringdonorcont__c msf_recencyrecurringdonordesc__c msf_recencytotalcont__c msf_recencytotalscore__c recordtypeid msf_contactinformationsummary__c msf_percomssummary__c msf_rfvdonor__c msf_rfvrecurringdonor__c title msf_scoringrfvdonor__c msf_scoringrfvrecurringdonor__c msf_scoringrvtotal__c msf_mailingsegment__c msf_legacyconfidentiality__c msf_membertype__c npo02__totaloppamount__c npo02__oppamountthisyear__c npo02__oppamount2yearsago__c npo02__oppamountlastyear__c npo02__best_gift_year_total__c msf_totalfiscaloppamount__c msf_lastannualizedquota__c msf_valuetotalcont__c msf_valuetotaldesc__c msf_valuedonorcont__c msf_valuedonordesc__c msf_lastyeardonorvalue__c msf_maximumdonorvalue__c msf_averagedonorvalue__c msf_lifetime__c msf_commitment__c
0 0033Y00002uqxIJQAY 19.0 2005 7013Y000001mrCzQAI Otro Otro False 0.0 Exdonante Nunca False 2005-02-28 2020-03-28 2005-01-07 None None 2005-01-07 None None None None None 2005-01-07 None None None Male 10165165 ESP 0.0 0.0 NaN 50.0 NaN NaN a0l0O00000k727RQAQ 50.0 Muy bajo 50 - 100 2.0 PONTEVEDRA NaN NaN NaN NaN False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False True False 7013Y000001mrCzQAI NaN Reactivación/conversión EXDonantes MASS False False False 6756.0 +10años NaN Nunca 6756.0 1.0 0120O000000LBoCQAW Sólo correo Todo 112 000 1.5 0.0 1.8 DON MUY ANTIGUOS False Exdonante 50.0 0.0 0.0 0.0 50.0 50.0 NaN 50.0 Muy bajo 50.0 Muy bajo NaN 50.0 50.0 0.0 0.0
1 0033Y00002uqxIRQAY 19.0 2005 7013Y000001mrCzQAI Otro Otro False 0.0 Exdonante Nunca False 2005-02-28 2020-03-28 2005-01-07 None None 2005-01-07 None None None None None 2005-01-07 None None None Female 10165173 ESP 0.0 0.0 NaN 30.0 NaN NaN a0l0O00000k727RQAQ 30.0 Muy bajo 0,10 - 50 1.0 MADRID NaN NaN NaN NaN False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False True False 7013Y000001mrCzQAI NaN Reactivación/conversión EXDonantes MASS False False False 6756.0 +10años NaN Nunca 6756.0 1.0 0120O000000LBoCQAW Sólo correo Todo 111 000 1.0 0.0 1.0 DON MUY ANTIGUOS False Exdonante 30.0 0.0 0.0 0.0 30.0 30.0 NaN 30.0 Muy bajo 30.0 Muy bajo NaN 30.0 30.0 0.0 0.0
2 0033Y00002uqxIZQAY 19.0 2005 7013Y000001mrCzQAI Otro Otro False 0.0 Exdonante Nunca False 2005-02-28 2020-03-28 2005-01-05 None None 2009-11-20 None None None None None 2005-01-05 None None None Other 10165185 ESP 0.0 0.0 NaN 70.0 NaN NaN a0l0O00000k727RQAQ 3320.0 Muy Alto 3.000 - 10.000 8.0 SEVILLA NaN NaN NaN NaN False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False True False 7013Y000001mrCzQAI NaN Empresas y Colectivos Mass False False False 4978.0 +10años NaN Nunca 4978.0 1.0 0120O000000LBoDQAW Teléfono+Correo Todo 125 000 3.3 0.0 4.2 EMPRESAS NO SOCIAS False Exdonante 3320.0 0.0 0.0 0.0 3050.0 3320.0 NaN 270.0 Medio 270.0 Medio NaN 3000.0 830.0 4.0 0.0
3 0033Y00002uqxIhQAI 17.0 2010 7013Y000001mrCzQAI Otro Otro False 0.0 Exdonante Nunca False 2005-02-28 2020-03-28 2007-01-03 None None 2012-01-20 None None None None None 2007-01-03 None None None Male 10165201 CAT 0.0 0.0 NaN 120.0 NaN NaN a0l0O00000k727RQAQ 720.0 Alto 500 - 1.000 6.0 LLEIDA NaN NaN NaN NaN False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False True False 7013Y000001mrCzQAI NaN Reactivación/conversión EXDonantes MASS False False False 4187.0 +10años NaN Nunca 4187.0 1.0 0120O000000LBoCQAW Sólo correo Todo 114 000 2.5 0.0 3.4 DON MUY ANTIGUOS False Exdonante 720.0 0.0 0.0 0.0 240.0 720.0 NaN 120.0 Bajo 120.0 Bajo NaN 120.0 120.0 5.0 1.0
4 0033Y00002uqxIpQAI 18.0 2005 7013Y000001mrFFQAY Otro False 0.0 Exdonante Nunca False 2005-03-01 2020-03-28 2005-03-01 None None 2005-03-01 None None None None None 2005-03-01 None None None Other 10165216 ESP 0.0 0.0 NaN 100.0 NaN NaN a0l0O00000k727RQAQ 100.0 Muy bajo 100 - 120 3.0 NaN NaN NaN NaN True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True True False 7013Y000001mrFFQAY NaN Reactivación/conversión EXDonantes MASS False False False 6703.0 +10años NaN Nunca 6703.0 1.0 0120O000000LBoCQAW No data Nada 113 000 2.0 0.0 2.6 DON MUY ANTIGUOS False Exdonante 100.0 0.0 0.0 0.0 100.0 100.0 NaN 100.0 Muy bajo 100.0 Muy bajo NaN 100.0 100.0 0.0 0.0
In [4]:
df_contactos.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1803419 entries, 0 to 1803418
Columns: 139 entries, id to msf_commitment__c
dtypes: bool(57), float64(36), object(46)
memory usage: 1.2+ GB
In [5]:
columnas_contactos =  df_contactos.columns.tolist()
columnas_contactos
Out[5]:
['id',
 'msf_seniority__c',
 'npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_entrycampaign__c',
 'msf_firstcampaignrecurringdonorchannel__c',
 'leadsource',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_returnedmail__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'npsp__deceased__c',
 'msf_begindatemsf__c',
 'msf_fechacambiolevelrelacion__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'msf_lastdonationunique__c',
 'gender__c',
 'msf_crmexternalid__c',
 'msf_languagepreferer__c',
 'npo02__largestamount__c',
 'npo02__smallestamount__c',
 'npsp__first_soft_credit_amount__c',
 'npo02__lastoppamount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_relationshiplevel__c',
 'msf_ltvcont__c',
 'msf_ltvdesc__c',
 'msf_ltvscore__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'msf_contactdeletereason__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_noagradecimientosmi__c',
 'msf_noagradecimientoscp__c',
 'msf_noagradecimientosemail__c',
 'msf_noagradecimientossms__c',
 'msf_noagradecimientostelefono__c',
 'msf_nocaptacionfondoscp__c',
 'msf_nocaptacionfondosemail__c',
 'msf_nocaptacionfondosmi__c',
 'msf_nocaptacionfondossms__c',
 'msf_nocartasplanrelacioncp__c',
 'msf_nocertificadofiscalcp__c',
 'msf_nocertificadofiscalemail__c',
 'msf_nocertificadofiscalmi__c',
 'msf_nocertificadofiscalsms__c',
 'msf_nocesionimagenpromocion__c',
 'msf_nocomunicacionesonetooneemail__c',
 'msf_nocomunicaconesonetoonemi__c',
 'msf_nocomunicacionesonetoonecp__c',
 'msf_nocomunicaconesonetoonesms__c',
 'msf_nocomunicacionesonetoonetelefono__c',
 'msf_noemailingstematicosemail__c',
 'msf_noencuestaestudioconcursoemail__c',
 'msf_noencuestaestudioconcursomi__c',
 'msf_nollamadasbienvenidasencuestasotras__c',
 'msf_noencuestaestudioconcursosms__c',
 'msf_noencuestaestudioconcursotelefono__c',
 'msf_noinformaciontestamentaria__c',
 'msf_noinvitacioneseventosmi__c',
 'msf_noinvitacioneseventoscp__c',
 'msf_noinvitacioneseventosemail__c',
 'msf_noinvitacioneseventossms__c',
 'msf_noinvitacioneseventostelefono__c',
 'msf_nomailingstematicoscp__c',
 'msf_nomemoriacp__c',
 'msf_nomemoriaemail__c',
 'msf_nomemoriami__c',
 'msf_nomemoriasms__c',
 'msf_nomensajesplanrelacionsms__c',
 'msf_nomensajestematicosmi__c',
 'msf_nomensajestematicossms__c',
 'msf_nonewsletteremail__c',
 'msf_noplanrelacionemail__c',
 'msf_nomensajesplanrelacionmi__c',
 'msf_noplanrelaciontelefono__c',
 'msf_norevistacp__c',
 'msf_norevistaemail__c',
 'msf_norevistami__c',
 'msf_norevistasms__c',
 'msf_notelemarketingcaptacionfondos__c',
 'msf_hasfirstdonation__c',
 'msf_hasfirstnewrecurringdonation__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_program__c',
 'msf_programaherencias__c',
 'msf_programais__c',
 'msf_pressurecomplaint__c',
 'msf_recencydonorcont__c',
 'msf_recencydonordesc__c',
 'msf_recencyrecurringdonorcont__c',
 'msf_recencyrecurringdonordesc__c',
 'msf_recencytotalcont__c',
 'msf_recencytotalscore__c',
 'recordtypeid',
 'msf_contactinformationsummary__c',
 'msf_percomssummary__c',
 'msf_rfvdonor__c',
 'msf_rfvrecurringdonor__c',
 'title',
 'msf_scoringrfvdonor__c',
 'msf_scoringrfvrecurringdonor__c',
 'msf_scoringrvtotal__c',
 'msf_mailingsegment__c',
 'msf_legacyconfidentiality__c',
 'msf_membertype__c',
 'npo02__totaloppamount__c',
 'npo02__oppamountthisyear__c',
 'npo02__oppamount2yearsago__c',
 'npo02__oppamountlastyear__c',
 'npo02__best_gift_year_total__c',
 'msf_totalfiscaloppamount__c',
 'msf_lastannualizedquota__c',
 'msf_valuetotalcont__c',
 'msf_valuetotaldesc__c',
 'msf_valuedonorcont__c',
 'msf_valuedonordesc__c',
 'msf_lastyeardonorvalue__c',
 'msf_maximumdonorvalue__c',
 'msf_averagedonorvalue__c',
 'msf_lifetime__c',
 'msf_commitment__c']
In [6]:
# Se revisa el nº total de registros y columnas de la tabla "Contactos"
df_contactos.shape
Out[6]:
(1803419, 139)
In [7]:
# Se analizan la cantidad de nulos en cada variable del dataset
nulos = df_contactos.isnull().sum()
nulos
Out[7]:
id                                                  0
msf_seniority__c                                    0
npo02__best_gift_year__c                            0
msf_birthyear__c                                    0
msf_entrycampaign__c                                0
msf_firstcampaignrecurringdonorchannel__c           0
leadsource                                          0
msf_firstcampaigncolaborationchannel__c             0
msf_returnedmail__c                                 0
npo02__averageamount__c                             0
msf_isactivedonor__c                                0
msf_isactiverecurringdonor__c                       0
npsp__deceased__c                                   0
msf_begindatemsf__c                                 1
msf_fechacambiolevelrelacion__c                  2204
msf_datefirstdonation__c                      1195087
msf_datefirstrecurringdonorquota__c            858069
msf_datelastrecurringdonorquota__c             858069
msf_datelastdonation__c                       1175962
npsp__largest_soft_credit_date__c             1803419
npsp__first_soft_credit_date__c               1803419
msf_entrydatecurrentrecurringdonor__c          809767
npsp__last_soft_credit_date__c                1803419
msf_firstentrydaterecurringdonor__c            809975
npo02__firstclosedate__c                       506557
msf_lastrecurringdonationdate__c              1231036
npo02__lastclosedate__c                       1803419
msf_lastdonationunique__c                     1803419
gender__c                                           0
msf_crmexternalid__c                                0
msf_languagepreferer__c                             0
npo02__largestamount__c                             0
npo02__smallestamount__c                            0
npsp__first_soft_credit_amount__c             1803419
npo02__lastoppamount__c                          3626
npsp__last_soft_credit_amount__c              1803419
msf_annualizedquotachange__c                  1145924
msf_relationshiplevel__c                            0
msf_ltvcont__c                                 507098
msf_ltvdesc__c                                      0
msf_ltvscore__c                                     0
mailingstate                                        0
npsp__largest_soft_credit_amount__c           1803419
msf_contactdeletereason__c                          0
npo02__soft_credit_last_year__c               1803419
npo02__soft_credit_this_year__c               1803419
npo02__soft_credit_two_years_ago__c           1803419
msf_noagradecimientosmi__c                          0
msf_noagradecimientoscp__c                          0
msf_noagradecimientosemail__c                       0
msf_noagradecimientossms__c                         0
msf_noagradecimientostelefono__c                    0
msf_nocaptacionfondoscp__c                          0
msf_nocaptacionfondosemail__c                       0
msf_nocaptacionfondosmi__c                          0
msf_nocaptacionfondossms__c                         0
msf_nocartasplanrelacioncp__c                       0
msf_nocertificadofiscalcp__c                        0
msf_nocertificadofiscalemail__c                     0
msf_nocertificadofiscalmi__c                        0
msf_nocertificadofiscalsms__c                       0
msf_nocesionimagenpromocion__c                      0
msf_nocomunicacionesonetooneemail__c                0
msf_nocomunicaconesonetoonemi__c                    0
msf_nocomunicacionesonetoonecp__c                   0
msf_nocomunicaconesonetoonesms__c                   0
msf_nocomunicacionesonetoonetelefono__c             0
msf_noemailingstematicosemail__c                    0
msf_noencuestaestudioconcursoemail__c               0
msf_noencuestaestudioconcursomi__c                  0
msf_nollamadasbienvenidasencuestasotras__c          0
msf_noencuestaestudioconcursosms__c                 0
msf_noencuestaestudioconcursotelefono__c            0
msf_noinformaciontestamentaria__c                   0
msf_noinvitacioneseventosmi__c                      0
msf_noinvitacioneseventoscp__c                      0
msf_noinvitacioneseventosemail__c                   0
msf_noinvitacioneseventossms__c                     0
msf_noinvitacioneseventostelefono__c                0
msf_nomailingstematicoscp__c                        0
msf_nomemoriacp__c                                  0
msf_nomemoriaemail__c                               0
msf_nomemoriami__c                                  0
msf_nomemoriasms__c                                 0
msf_nomensajesplanrelacionsms__c                    0
msf_nomensajestematicosmi__c                        0
msf_nomensajestematicossms__c                       0
msf_nonewsletteremail__c                            0
msf_noplanrelacionemail__c                          0
msf_nomensajesplanrelacionmi__c                     0
msf_noplanrelaciontelefono__c                       0
msf_norevistacp__c                                  0
msf_norevistaemail__c                               0
msf_norevistami__c                                  0
msf_norevistasms__c                                 0
msf_notelemarketingcaptacionfondos__c               0
msf_hasfirstdonation__c                             0
msf_hasfirstnewrecurringdonation__c                 0
msf_firstcampaignentryrecurringdonor__c             0
msf_firstcampaingcolaboration__c                    0
msf_firstannualizedquota__c                    841860
msf_program__c                                      0
msf_programaherencias__c                            0
msf_programais__c                                   0
msf_pressurecomplaint__c                            0
msf_recencydonorcont__c                       1177448
msf_recencydonordesc__c                             0
msf_recencyrecurringdonorcont__c               868629
msf_recencyrecurringdonordesc__c                    0
msf_recencytotalcont__c                        507444
msf_recencytotalscore__c                            0
recordtypeid                                        0
msf_contactinformationsummary__c                    0
msf_percomssummary__c                               0
msf_rfvdonor__c                                     0
msf_rfvrecurringdonor__c                            0
title                                               0
msf_scoringrfvdonor__c                              0
msf_scoringrfvrecurringdonor__c                     0
msf_scoringrvtotal__c                               0
msf_mailingsegment__c                               0
msf_legacyconfidentiality__c                        0
msf_membertype__c                                   0
npo02__totaloppamount__c                            1
npo02__oppamountthisyear__c                         0
npo02__oppamount2yearsago__c                        0
npo02__oppamountlastyear__c                         0
npo02__best_gift_year_total__c                 507444
msf_totalfiscaloppamount__c                         3
msf_lastannualizedquota__c                     850655
msf_valuetotalcont__c                          450726
msf_valuetotaldesc__c                               0
msf_valuedonorcont__c                         1178350
msf_valuedonordesc__c                               0
msf_lastyeardonorvalue__c                     1690288
msf_maximumdonorvalue__c                      1177577
msf_averagedonorvalue__c                      1177577
msf_lifetime__c                                507098
msf_commitment__c                              223407
dtype: int64
In [8]:
# Comprobamos si existen duplicados
print(len(df_contactos))
print(len(df_contactos.drop_duplicates()))
1803419
1803419
No existen duplicados en el dataset

Carga los datos de la tabla "recurring_donation"¶

In [9]:
# Importamos dataset de RECURRING DONATION
df_re_donation = pd.read_parquet("MSF_RecurringDonation.parquet")
df_re_donation.head()
Out[9]:
id isdeleted msf_annualizedquota__c msf_cancelationdate__c msf_cancelationreason__c msf_currentcampaign__c msf_currentleadsource1__c msf_currentquotamodification__c msf_leadsource1__c msf_memberid__c npe03__amount__c npe03__contact__c npe03__date_established__c npe03__installment_period__c npe03__last_payment_date__c npe03__next_payment_date__c npe03__open_ended_status__c npe03__paid_amount__c npe03__recurring_donation_campaign__c npe03__total_paid_installments__c npsp4hub__payment_method__c
0 a093Y00001RhiLNQAZ False 72.00 2011-02-04 Impago aportaciones 7013Y000001mqt9QAA Telemarketing a1y3Y000004sW6pQAE Cupón 9969053 6.00 0033Y00002uppXLQAY 2011-01-07 Monthly 2011-03-01 None Closed 0.0 7013Y000001mqt9QAA 0.0 Direct Debit
1 a093Y00001RhiLVQAZ False 72.12 2010-11-09 3 Obs/Tcs Devueltas 7013Y000001mqy2QAA Cupón a1y3Y000004uVpAQAU Cupón 9969066 6.01 0033Y00002uppXXQAY 2000-02-01 Monthly 2010-12-01 None Closed 0.0 7013Y000001mrOjQAI 0.0 Direct Debit
2 a093Y00001RhiLdQAJ False 144.24 2005-05-09 Impago aportaciones 7013Y000001mrOjQAI Cupón a1y3Y000004tQbhQAE Cupón 9969086 12.02 0033Y00002uppXqQAI 2000-02-01 Monthly 2005-05-04 None Closed 0.0 7013Y000001mrOjQAI 0.0 Direct Debit
3 a093Y00001RhiLoQAJ False 72.12 2001-05-29 3 Obs/Tcs Devueltas 7013Y000001mrOjQAI Cupón a1y3Y000004sun6QAA Cupón 9969107 6.01 0033Y00002uppYCQAY 2000-02-01 Monthly 2001-05-01 None Closed 0.0 7013Y000001mrOjQAI 0.0 Direct Debit
4 a093Y00001RhiLtQAJ False 72.12 2010-05-03 Incidencias Web 7013Y000001mrOjQAI Cupón a1y3Y000004sD40QAE Cupón 9969113 6.01 0033Y00002uppYIQAY 2000-02-01 Monthly 2010-04-01 None Closed 0.0 7013Y000001mrOjQAI 0.0 Direct Debit
In [10]:
df_re_donation.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1198207 entries, 0 to 1198206
Data columns (total 21 columns):
 #   Column                                 Non-Null Count    Dtype  
---  ------                                 --------------    -----  
 0   id                                     1198207 non-null  object 
 1   isdeleted                              1198207 non-null  bool   
 2   msf_annualizedquota__c                 1198207 non-null  float64
 3   msf_cancelationdate__c                 715201 non-null   object 
 4   msf_cancelationreason__c               1198207 non-null  object 
 5   msf_currentcampaign__c                 1198207 non-null  object 
 6   msf_currentleadsource1__c              1198207 non-null  object 
 7   msf_currentquotamodification__c        1198207 non-null  object 
 8   msf_leadsource1__c                     1198207 non-null  object 
 9   msf_memberid__c                        1198207 non-null  object 
 10  npe03__amount__c                       1198207 non-null  float64
 11  npe03__contact__c                      1198207 non-null  object 
 12  npe03__date_established__c             1198207 non-null  object 
 13  npe03__installment_period__c           1198207 non-null  object 
 14  npe03__last_payment_date__c            1122597 non-null  object 
 15  npe03__next_payment_date__c            483302 non-null   object 
 16  npe03__open_ended_status__c            1198207 non-null  object 
 17  npe03__paid_amount__c                  1196308 non-null  float64
 18  npe03__recurring_donation_campaign__c  1198207 non-null  object 
 19  npe03__total_paid_installments__c      1196308 non-null  float64
 20  npsp4hub__payment_method__c            1198207 non-null  object 
dtypes: bool(1), float64(4), object(16)
memory usage: 184.0+ MB
In [11]:
columnas_re_donation =  df_re_donation.columns.tolist()
columnas_re_donation
Out[11]:
['id',
 'isdeleted',
 'msf_annualizedquota__c',
 'msf_cancelationdate__c',
 'msf_cancelationreason__c',
 'msf_currentcampaign__c',
 'msf_currentleadsource1__c',
 'msf_currentquotamodification__c',
 'msf_leadsource1__c',
 'msf_memberid__c',
 'npe03__amount__c',
 'npe03__contact__c',
 'npe03__date_established__c',
 'npe03__installment_period__c',
 'npe03__last_payment_date__c',
 'npe03__next_payment_date__c',
 'npe03__open_ended_status__c',
 'npe03__paid_amount__c',
 'npe03__recurring_donation_campaign__c',
 'npe03__total_paid_installments__c',
 'npsp4hub__payment_method__c']
In [12]:
# Se revisa el nº total de registros y columnas de la tabla "donaciones recurrentes"
df_re_donation.shape
Out[12]:
(1198207, 21)
In [13]:
# Se analizan la cantidad de nulos en cada variable del dataset
nulos = df_re_donation.isnull().sum()
nulos
Out[13]:
id                                            0
isdeleted                                     0
msf_annualizedquota__c                        0
msf_cancelationdate__c                   483006
msf_cancelationreason__c                      0
msf_currentcampaign__c                        0
msf_currentleadsource1__c                     0
msf_currentquotamodification__c               0
msf_leadsource1__c                            0
msf_memberid__c                               0
npe03__amount__c                              0
npe03__contact__c                             0
npe03__date_established__c                    0
npe03__installment_period__c                  0
npe03__last_payment_date__c               75610
npe03__next_payment_date__c              714905
npe03__open_ended_status__c                   0
npe03__paid_amount__c                      1899
npe03__recurring_donation_campaign__c         0
npe03__total_paid_installments__c          1899
npsp4hub__payment_method__c                   0
dtype: int64
In [14]:
# Comprobamos si existen duplicados
print(len(df_re_donation))
print(len(df_re_donation.drop_duplicates()))
1198207
1198207
No existen duplicados en el dataset

Carga los datos de la tabla "Modificacion de cuota"¶

In [15]:
# Importamos dataset de MODIFICACION DE CUOTA 
df_mod_cuota = pd.read_parquet("MSF_QuotaModification.parquet")
df_mod_cuota.head()
Out[15]:
id isdeleted name msf_recurringdonation__c msf_campaigninfluence__c msf_changeamount__c msf_changeannualizedquota__c msf_changetype__c msf_leadsource1__c msf_leadsource2__c msf_leadsource3__c msf_newamount__c msf_newannualizedquota__c msf_newrecurringperiod__c msf_contactid__c msf_changedate__c
0 a1y3Y000001sAtxQAE False A - 15948014621775 a093Y00001RZ7cgQAD 7013Y000001mquNQAQ 5.0 60.0 Increase Teléfono web Teléfono web Teléfono 20.0 240.0 Monthly 0033Y00002uNQ6CQAW 2020-04-02
1 a1y3Y000001sAu5QAE False A - 159480146217713 a093Y00001RZ7eLQAT 7013Y000001mrgcQAA 20.0 240.0 Increase Telemarketing Telemarketing Teléfono 40.0 480.0 Monthly 0033Y00002uNQJ6QAO 2020-05-03
2 a1y3Y000001sAuDQAU False D - 159480146217721 a093Y00001RZ7kmQAD 7013Y000001mrgcQAA 8.0 104.0 Decrease Telemarketing Telemarketing Teléfono 68.0 136.0 Semestral 0033Y00002uNREvQAO 2020-04-02
3 a1y3Y000001sAuLQAU False A - 159480146217729 a093Y00001RZ8NEQA1 7013Y000001mqtMQAQ 35.0 35.0 Increase Telemarketing Telemarketing Teléfono 110.0 110.0 Yearly 0033Y00002uNSmdQAG 2020-03-02
4 a1y3Y000001sAuTQAU False A - 159480146217737 a093Y00001RZ9UeQAL 7013Y000001mrgcQAA 2.0 24.0 Increase Telemarketing Telemarketing Teléfono 19.0 228.0 Monthly 0033Y00002uNV7OQAW 2020-07-01
In [16]:
df_mod_cuota.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2003019 entries, 0 to 2003018
Data columns (total 16 columns):
 #   Column                        Dtype  
---  ------                        -----  
 0   id                            object 
 1   isdeleted                     bool   
 2   name                          object 
 3   msf_recurringdonation__c      object 
 4   msf_campaigninfluence__c      object 
 5   msf_changeamount__c           float64
 6   msf_changeannualizedquota__c  float64
 7   msf_changetype__c             object 
 8   msf_leadsource1__c            object 
 9   msf_leadsource2__c            object 
 10  msf_leadsource3__c            object 
 11  msf_newamount__c              float64
 12  msf_newannualizedquota__c     float64
 13  msf_newrecurringperiod__c     object 
 14  msf_contactid__c              object 
 15  msf_changedate__c             object 
dtypes: bool(1), float64(4), object(11)
memory usage: 231.1+ MB
In [17]:
columnas_mod_cuota =  df_mod_cuota.columns.tolist()
columnas_mod_cuota
Out[17]:
['id',
 'isdeleted',
 'name',
 'msf_recurringdonation__c',
 'msf_campaigninfluence__c',
 'msf_changeamount__c',
 'msf_changeannualizedquota__c',
 'msf_changetype__c',
 'msf_leadsource1__c',
 'msf_leadsource2__c',
 'msf_leadsource3__c',
 'msf_newamount__c',
 'msf_newannualizedquota__c',
 'msf_newrecurringperiod__c',
 'msf_contactid__c',
 'msf_changedate__c']
In [18]:
# Se revisa el nº total de registros y columnas de la tabla "donaciones recurrentes"
df_mod_cuota.shape
Out[18]:
(2003019, 16)
In [19]:
# Se analizan la cantidad de nulos en cada variable del dataset
nulos = df_mod_cuota.isnull().sum()
nulos
Out[19]:
id                                0
isdeleted                         0
name                              0
msf_recurringdonation__c          0
msf_campaigninfluence__c          0
msf_changeamount__c               0
msf_changeannualizedquota__c      0
msf_changetype__c                 0
msf_leadsource1__c                0
msf_leadsource2__c                0
msf_leadsource3__c                0
msf_newamount__c                  0
msf_newannualizedquota__c         0
msf_newrecurringperiod__c         0
msf_contactid__c                  0
msf_changedate__c               186
dtype: int64
In [20]:
# Comprobamos si existen duplicados
print(len(df_mod_cuota))
print(len(df_mod_cuota.drop_duplicates()))
2003019
2003019
No existen duplicados en el dataset

Carga los datos de la tabla "Campaña"¶

In [21]:
# Importamos dataset de CAMPAÑAS
df_Campaign = pd.read_parquet("MSF_Campaign.parquet")
df_Campaign.head()
Out[21]:
id msf_attribute_1__c msf_attribute_2__c msf_attribute_3__c msf_attribute_4__c msf_attribute_5__c msf_campaigndonationreporting__c msf_campaignentryreporting__c msf_canalsalidaconcatenado__c msf_isemergency__c msf_isonline__c msf_objective__c msf_objectivepublic__c msf_outboundchannel1__c msf_outboundchannel2__c msf_ownby__c msf_previousstepchannel__c msf_promoterindividual__c msf_provider__c msf_segment__c msf_thematic__c ownerid recordtypeid status
0 7013Y000001mrHWQAY Afiliación Leads 23-Digital Orgánico 23-Digital Orgánico Afiliación - False Si Captación de socios o donantes Afiliación Digital Frio individuos 90 0050O000009jTv8QAE 0120O000000kNMGQA2 Completed
1 7013Y000001mrEdQAI 16-Captación off resto 18-Captación off resto Prensa o cupón - False No Captación de socios o donantes Prensa o cupón Captación Frío individuos 90 0050O000009jTv8QAE 0120O000000kNMGQA2 Completed
2 7013Y000001mrErQAI 16-Captación off resto 18-Captación off resto Prensa o cupón - False No Captación de socios o donantes Prensa o cupón Captación Frío individuos 05 0050O000009jTv8QAE 0120O000000kNMGQA2 Completed
3 7013Y000001mrLqQAI Diario de ibiza 16-Captación off resto 18-Captación off resto Encarte - False No Captación de socios o donantes Encarte Captación Frío individuos 90 0050O000009jTv8QAE 0120O000000kNMGQA2 Completed
4 7013Y000001mrFYQAY 16-Captación off resto 18-Captación off resto Mailing - False No Captación de socios o donantes Mailing Captación Frío individuos 90 0050O000009jTv8QAE 0120O000000kNMGQA2 Completed
In [22]:
df_Campaign.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11501 entries, 0 to 11500
Data columns (total 24 columns):
 #   Column                            Non-Null Count  Dtype 
---  ------                            --------------  ----- 
 0   id                                11501 non-null  object
 1   msf_attribute_1__c                11501 non-null  object
 2   msf_attribute_2__c                11501 non-null  object
 3   msf_attribute_3__c                11501 non-null  object
 4   msf_attribute_4__c                11501 non-null  object
 5   msf_attribute_5__c                11501 non-null  object
 6   msf_campaigndonationreporting__c  11501 non-null  object
 7   msf_campaignentryreporting__c     11501 non-null  object
 8   msf_canalsalidaconcatenado__c     11501 non-null  object
 9   msf_isemergency__c                11501 non-null  bool  
 10  msf_isonline__c                   11501 non-null  object
 11  msf_objective__c                  11501 non-null  object
 12  msf_objectivepublic__c            11501 non-null  object
 13  msf_outboundchannel1__c           11501 non-null  object
 14  msf_outboundchannel2__c           11501 non-null  object
 15  msf_ownby__c                      11501 non-null  object
 16  msf_previousstepchannel__c        11501 non-null  object
 17  msf_promoterindividual__c         11501 non-null  object
 18  msf_provider__c                   11501 non-null  object
 19  msf_segment__c                    11501 non-null  object
 20  msf_thematic__c                   11501 non-null  object
 21  ownerid                           11501 non-null  object
 22  recordtypeid                      11501 non-null  object
 23  status                            11501 non-null  object
dtypes: bool(1), object(23)
memory usage: 2.0+ MB
In [23]:
columnas_Campaign =  df_Campaign.columns.tolist()
columnas_Campaign
Out[23]:
['id',
 'msf_attribute_1__c',
 'msf_attribute_2__c',
 'msf_attribute_3__c',
 'msf_attribute_4__c',
 'msf_attribute_5__c',
 'msf_campaigndonationreporting__c',
 'msf_campaignentryreporting__c',
 'msf_canalsalidaconcatenado__c',
 'msf_isemergency__c',
 'msf_isonline__c',
 'msf_objective__c',
 'msf_objectivepublic__c',
 'msf_outboundchannel1__c',
 'msf_outboundchannel2__c',
 'msf_ownby__c',
 'msf_previousstepchannel__c',
 'msf_promoterindividual__c',
 'msf_provider__c',
 'msf_segment__c',
 'msf_thematic__c',
 'ownerid',
 'recordtypeid',
 'status']
In [24]:
# Se revisa el nº total de registros y columnas de la tabla "donaciones recurrentes"
df_Campaign.shape
Out[24]:
(11501, 24)
In [25]:
# Se analizan la cantidad de nulos en cada variable del dataset
nulos = df_Campaign.isnull().sum()
nulos
Out[25]:
id                                  0
msf_attribute_1__c                  0
msf_attribute_2__c                  0
msf_attribute_3__c                  0
msf_attribute_4__c                  0
msf_attribute_5__c                  0
msf_campaigndonationreporting__c    0
msf_campaignentryreporting__c       0
msf_canalsalidaconcatenado__c       0
msf_isemergency__c                  0
msf_isonline__c                     0
msf_objective__c                    0
msf_objectivepublic__c              0
msf_outboundchannel1__c             0
msf_outboundchannel2__c             0
msf_ownby__c                        0
msf_previousstepchannel__c          0
msf_promoterindividual__c           0
msf_provider__c                     0
msf_segment__c                      0
msf_thematic__c                     0
ownerid                             0
recordtypeid                        0
status                              0
dtype: int64
In [26]:
# Comprobamos si existen duplicados
print(len(df_Campaign))
print(len(df_Campaign.drop_duplicates()))
11501
11501
No existen duplicados en el dataset

Carga los datos de la tabla "Tareas"¶

In [27]:
# Importamos dataset de TAREAS
df_tareas2 = pd.read_csv("tarea_aumento_2.csv", names=['msf_Objective__c','msf_CloseType__c','id','ActivityDate','msf_Channel__c','msf_Campaign__c','msf_StartDate__c','Status','WhoId'])
df_tareas3 = pd.read_csv("tarea_aumento_3.csv", names=['msf_Objective__c','msf_CloseType__c','id','ActivityDate','msf_Channel__c','msf_Campaign__c','msf_StartDate__c','Status','WhoId'])
df_tareas4 = pd.read_csv("tarea_aumento_4.csv", names=['msf_Objective__c','msf_CloseType__c','id','ActivityDate','msf_Channel__c','msf_Campaign__c','msf_StartDate__c','Status','WhoId'])
df_tareas5 = pd.read_csv("tarea_aumento_5.csv", names=['msf_Objective__c','msf_CloseType__c','id','ActivityDate','msf_Channel__c','msf_Campaign__c','msf_StartDate__c','Status','WhoId'])
df_tareas6 = pd.read_csv("tarea_aumento_6.csv", names=['msf_Objective__c','msf_CloseType__c','id','ActivityDate','msf_Channel__c','msf_Campaign__c','msf_StartDate__c','Status','WhoId'])
C:\Users\marta\AppData\Local\Temp\ipykernel_2548\2290543.py:3: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.
  df_tareas3 = pd.read_csv("tarea_aumento_3.csv", names=['msf_Objective__c','msf_CloseType__c','id','ActivityDate','msf_Channel__c','msf_Campaign__c','msf_StartDate__c','Status','WhoId'])
In [28]:
# Unificamos las particiones en una unica tabla TAREA
df_tareas = pd.concat([df_tareas2,df_tareas3,df_tareas4,df_tareas5,df_tareas6])
df_tareas.head()
Out[28]:
msf_Objective__c msf_CloseType__c id ActivityDate msf_Channel__c msf_Campaign__c msf_StartDate__c Status WhoId
0 Petición económica-Upgrade Socio Positivo 00T3Y00005x0TjkUAE 2021-03-26 Llamada 7013Y000001n865QAA 2021-03-01 Realizada 0033Y00002unXvsQAE
1 Petición económica-Upgrade Socio Negativo 00T3Y00005x0UAkUAM 2021-03-31 Llamada 7013Y000001n860QAA 2021-03-01 Realizada 0033Y00002unKClQAM
2 Petición económica-Upgrade Socio Positivo 00T3Y00005x0UIeUAM 2021-03-23 Llamada 7013Y000001n865QAA 2021-03-01 Realizada 0033Y00002upuiLQAQ
3 Petición económica-Upgrade Socio Negativo 00T3Y00005x0UIfUAM 2021-03-29 Llamada 7013Y000001n865QAA 2021-03-01 Realizada 0033Y00002upuinQAA
4 Petición económica-Upgrade Socio Negativo 00T3Y00005x0UJuUAM 2021-03-04 Llamada 7013Y000001n865QAA 2021-03-01 Realizada 0033Y00002v6a4vQAA
In [29]:
columnas_tareas =  df_tareas.columns.tolist()
columnas_tareas
Out[29]:
['msf_Objective__c',
 'msf_CloseType__c',
 'id',
 'ActivityDate',
 'msf_Channel__c',
 'msf_Campaign__c',
 'msf_StartDate__c',
 'Status',
 'WhoId']
In [30]:
# Se revisa el nº total de registros y columnas de la tabla "tareas"
df_tareas.shape
Out[30]:
(2612004, 9)
In [31]:
# Se analizan la cantidad de nulos en cada variable del dataset
nulos = df_tareas.isnull().sum()
nulos
Out[31]:
msf_Objective__c          0
msf_CloseType__c      62202
id                        0
ActivityDate              0
msf_Channel__c            1
msf_Campaign__c      210116
msf_StartDate__c    1526897
Status                    0
WhoId                     0
dtype: int64
In [32]:
# Comprobamos si existen duplicados
print(len(df_tareas))
print(len(df_tareas.drop_duplicates()))
2612004
2612004
No existen duplicados en el dataset

Se va a analizar cada variable por tablas¶

Definición de funciones para el analisis exploratorio del dataset:
-> Se definirá la función conunt_nulos, que servirá para contar el nº de vacios y nulos de las variables del dataset.
-> Se definirá la función grafica_categorica, que servirá para plotear la distribucioes de las variables categoricas del dataset.
-> Se definirá la función freq_variables, que se utilizará para realizar un conteo de cada psoible valor de la variable.
In [33]:
# Contabilizacion de los nulos valores de la variable
def count_nulos(df,variable,list_delete):
    
    '''
    Función count_nulos

    Uso:
    Sirva para realizar un conteo de los registros nulos y vacios de una variable seleccionada del dataframe de entrada.

    Parametros entrada:
    - df : dataframe de entrada.
    - variable: nombre de la variable sobre la que se quiere graficar. Variable tipo categorica o numerica.

    Salida:
    Frase en la que se detectará el nº de nulos y vacios, asi como su porcentaje del total de la tabla.
        
    '''
        
    nulos = df[[variable]].isnull().sum()[0]
    vacios = (df[[var]] == '').sum(axis=0)[0]
    print(f"El nº de nulos para la variable {variable} es {nulos}. Lo que supone un {(nulos/df.shape[0])*100}%")
    print(f"El nº de vacios para la variable {var} es {vacios}. Lo que supone un {(vacios/df.shape[0])*100}%")
    
    if ((nulos + vacios) / df.shape[0]) *100 > 20:
        list_delete.append(var)
        return list_delete
In [34]:
# Contabilizacion de los posibles valores de la variable
def freq_variables(df,variable):
    
    '''
    Función freq_variables

    Uso:
    Sirva para realizar un conteo de los posibles valores de una variable seleccionada del dataframe de entrada.

    Parametros entrada:
    - df : dataframe de entrada.
    - variable: nombre de la variable sobre la que se quiere graficar. Variable tipo categorica o numerica.

    Salida:
    La salida es una tabla que contiene los posibles valores de la variable de entrada y el nº de registros de cada valor en el dataframe.
        
    '''
    afreq = df[variable].value_counts()
    pfreq = df[variable].value_counts(normalize = True)*100
    freq_report = pd.DataFrame({'# Tot':afreq, '% Tot':pfreq})
    return freq_report

1. Tabla recurring donation¶

In [35]:
# Vamos a analizar la tabla recurring donation
df = df_re_donation
In [36]:
# Se crea una lista por ahora vacia, en la que se irán añadiendo las variables que se van a eliminar del dataset por motivos varios: no utilidad, gran volumen de nulos, ...
col_to_delete_re_donation=list()
Analsis de distribución por variables
-> IsDeleted: Variable booleana
In [37]:
# Vamos a realizar analisis por cada variable
var = "isdeleted"
In [38]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable isdeleted es 0. Lo que supone un 0.0%
El nº de vacios para la variable isdeleted es 0. Lo que supone un 0.0%
In [39]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[39]:
# Tot % Tot
False 1198207 100.0
isdeleted: Sirve para descartar registros que hayan sido errores en caso de que tome valor true.
No hay ningun true por lo que se pueden considerar todos los registros. Se puede eliminar la variable al no aportar más valor.
In [40]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_re_donation.append(var)
col_to_delete_re_donation
Out[40]:
['isdeleted']
Analsis de distribución por variables
-> msf_AnnualizedQuota__c: Variable numerica
In [41]:
# Vamos a realizar analisis por cada variable
var = "msf_annualizedquota__c"
In [42]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable msf_annualizedquota__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_annualizedquota__c es 0. Lo que supone un 0.0%
In [43]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[43]:
# Tot % Tot
120.00 285404 23.819257
60.00 129853 10.837276
180.00 122956 10.261666
240.00 76435 6.379115
144.00 57831 4.826462
72.00 48674 4.062236
36.00 30804 2.570841
360.00 28630 2.389404
300.00 26549 2.215727
96.00 21951 1.831987
72.12 21267 1.774902
0.00 19245 1.606150
84.00 16695 1.393332
100.00 15786 1.317469
168.00 13694 1.142874
40.00 12404 1.035213
20.00 11682 0.974957
50.00 11473 0.957514
80.00 9212 0.768815
600.00 8751 0.730341
30.00 8515 0.710645
200.00 8126 0.678180
10.00 8036 0.670669
48.00 7544 0.629607
204.00 7497 0.625685
51.96 6882 0.574358
480.00 6742 0.562674
216.00 6595 0.550406
12.00 6240 0.520778
150.00 6048 0.504754
132.00 5844 0.487729
60.10 5371 0.448253
108.00 5310 0.443162
192.00 5175 0.431895
156.00 5067 0.422882
420.00 4893 0.408360
30.05 4827 0.402852
120.20 4787 0.399514
15.00 4202 0.350691
312.00 4182 0.349021
216.36 4000 0.333832
144.24 3959 0.330410
264.00 3940 0.328825
720.00 3649 0.304538
360.60 3442 0.287263
228.00 3352 0.279751
160.00 3125 0.260806
90.00 2964 0.247370
5.00 2670 0.222833
276.00 2531 0.211232
24.00 2191 0.182857
25.00 2143 0.178851
1200.00 2127 0.177515
140.00 2122 0.177098
70.00 2020 0.168585
18.03 2010 0.167751
400.00 1904 0.158904
3.00 1601 0.133616
384.00 1577 0.131613
540.00 1559 0.130111
240.40 1520 0.126856
288.00 1444 0.120513
75.00 1436 0.119846
250.00 1227 0.102403
90.15 1164 0.097145
336.00 1159 0.096728
252.00 1142 0.095309
324.00 1127 0.094057
260.00 946 0.078951
24.04 913 0.076197
48.08 883 0.073693
721.20 878 0.073276
6.00 835 0.069687
396.00 768 0.064096
500.00 746 0.062260
130.00 711 0.059339
110.00 678 0.056585
280.00 652 0.054415
18.00 616 0.051410
125.00 610 0.050909
840.00 609 0.050826
35.00 608 0.050742
220.00 598 0.049908
660.00 575 0.047988
45.00 565 0.047154
320.00 559 0.046653
150.25 500 0.041729
34.86 499 0.041646
36.06 493 0.041145
36.12 493 0.041145
32.00 478 0.039893
432.00 450 0.037556
900.00 428 0.035720
960.00 420 0.035052
108.24 415 0.034635
139.44 413 0.034468
65.00 409 0.034134
88.00 404 0.033717
28.00 402 0.033550
408.00 399 0.033300
1000.00 387 0.032298
170.00 385 0.032131
52.00 373 0.031130
42.00 356 0.029711
104.04 351 0.029294
210.00 333 0.027792
8.00 328 0.027374
780.00 328 0.027374
350.00 326 0.027207
173.04 316 0.026373
55.00 312 0.026039
180.36 309 0.025789
624.00 299 0.024954
444.00 296 0.024704
1080.00 294 0.024537
372.00 291 0.024286
175.00 287 0.023952
1800.00 287 0.023952
504.00 287 0.023952
800.00 279 0.023285
288.48 276 0.023034
12.02 275 0.022951
56.00 271 0.022617
14.00 262 0.021866
6.01 255 0.021282
22.00 252 0.021031
16.00 246 0.020531
85.00 243 0.020280
165.00 242 0.020197
348.00 234 0.019529
230.00 224 0.018695
456.00 220 0.018361
112.00 210 0.017526
2400.00 210 0.017526
520.00 209 0.017443
57.68 202 0.016859
180.30 201 0.016775
418.32 197 0.016441
72.24 185 0.015440
103.92 184 0.015356
92.00 182 0.015189
96.16 179 0.014939
1440.00 178 0.014856
9.00 177 0.014772
68.00 170 0.014188
7.00 168 0.014021
54.00 162 0.013520
36.08 159 0.013270
17.00 157 0.013103
152.00 157 0.013103
105.00 155 0.012936
4.00 153 0.012769
93.16 151 0.012602
340.00 143 0.011934
64.00 142 0.011851
135.00 141 0.011768
104.00 140 0.011684
516.00 139 0.011601
528.00 137 0.011434
128.00 135 0.011267
440.00 131 0.010933
225.00 127 0.010599
1081.80 126 0.010516
864.00 123 0.010265
1500.00 122 0.010182
552.00 122 0.010182
190.00 120 0.010015
115.00 118 0.009848
300.51 115 0.009598
224.00 115 0.009598
450.00 114 0.009514
62.00 113 0.009431
124.00 112 0.009347
432.72 108 0.009013
44.00 105 0.008763
148.00 103 0.008596
700.00 102 0.008513
270.00 101 0.008429
38.00 101 0.008429
66.00 98 0.008179
346.20 97 0.008095
492.00 93 0.007762
232.00 92 0.007678
21.00 92 0.007678
95.00 91 0.007595
11.00 91 0.007595
115.40 90 0.007511
1020.00 89 0.007428
468.00 88 0.007344
26.00 85 0.007094
380.00 84 0.007010
3600.00 83 0.006927
28.85 82 0.006844
108.12 80 0.006677
460.00 80 0.006677
76.00 80 0.006677
60.12 79 0.006593
576.00 77 0.006426
601.00 77 0.006426
34.85 77 0.006426
184.00 74 0.006176
42.07 72 0.006009
310.00 71 0.005926
192.32 70 0.005842
865.44 70 0.005842
155.00 69 0.005759
84.14 68 0.005675
480.80 68 0.005675
300.50 68 0.005675
136.00 67 0.005592
180.24 66 0.005508
330.00 66 0.005508
14.42 65 0.005425
12000.00 65 0.005425
2000.00 64 0.005341
164.00 63 0.005258
74.00 62 0.005174
13.00 61 0.005091
275.00 61 0.005091
390.00 58 0.004841
1320.00 58 0.004841
601.01 57 0.004757
139.40 57 0.004757
34.00 57 0.004757
54.09 56 0.004674
78.00 55 0.004590
27.00 53 0.004423
3000.00 52 0.004340
33.00 51 0.004256
57.70 50 0.004173
145.00 50 0.004173
576.96 50 0.004173
560.00 50 0.004173
126.00 49 0.004089
1442.40 48 0.004006
102.00 48 0.004006
640.00 47 0.003923
550.00 46 0.003839
564.00 43 0.003589
372.64 43 0.003589
248.00 43 0.003589
168.28 43 0.003589
212.00 42 0.003505
208.00 42 0.003505
63.00 42 0.003505
116.00 42 0.003505
648.00 42 0.003505
744.00 41 0.003422
12.04 40 0.003338
185.00 40 0.003338
82.00 39 0.003255
1803.00 39 0.003255
6000.00 38 0.003171
93.15 37 0.003088
620.00 37 0.003088
172.00 37 0.003088
162.00 37 0.003088
504.84 37 0.003088
361.44 36 0.003004
39.00 35 0.002921
364.00 35 0.002921
17.32 35 0.002921
290.00 34 0.002838
108.18 34 0.002838
84.60 32 0.002671
23.00 32 0.002671
75.96 32 0.002671
37.00 32 0.002671
188.00 31 0.002587
612.00 31 0.002587
1560.00 30 0.002504
792.00 30 0.002504
365.00 30 0.002504
196.00 30 0.002504
176.00 30 0.002504
636.00 29 0.002420
63.96 29 0.002420
375.00 28 0.002337
98.00 27 0.002253
1008.00 27 0.002253
325.00 27 0.002253
370.00 27 0.002253
650.00 26 0.002170
236.00 26 0.002170
46.00 25 0.002086
692.40 24 0.002003
240.41 24 0.002003
180.32 23 0.001920
36.04 23 0.001920
78.13 22 0.001836
67.00 22 0.001836
252.48 22 0.001836
392.00 21 0.001753
750.00 21 0.001753
936.00 21 0.001753
94.00 21 0.001753
1600.00 20 0.001669
272.00 20 0.001669
235.00 20 0.001669
215.00 20 0.001669
120.48 20 0.001669
1117.92 20 0.001669
174.00 19 0.001586
109.44 19 0.001586
60.24 19 0.001586
1140.00 19 0.001586
3606.12 19 0.001586
77.00 19 0.001586
2160.00 18 0.001502
9.02 18 0.001502
425.00 17 0.001419
108.20 17 0.001419
31.00 17 0.001419
60.08 17 0.001419
86.00 17 0.001419
15.03 17 0.001419
672.00 17 0.001419
3.01 17 0.001419
8.66 16 0.001335
418.20 16 0.001335
21.60 16 0.001335
684.00 16 0.001335
180.28 16 0.001335
760.00 15 0.001252
230.80 15 0.001252
51.00 15 0.001252
28.84 15 0.001252
205.00 15 0.001252
1680.00 14 0.001168
86.52 14 0.001168
680.00 14 0.001168
649.08 14 0.001168
7200.00 14 0.001168
4.33 14 0.001168
73.00 14 0.001168
182.40 14 0.001168
244.00 14 0.001168
1260.00 14 0.001168
1152.00 14 0.001168
5196.00 13 0.001085
3900.00 13 0.001085
87.00 13 0.001085
53.00 13 0.001085
4800.00 13 0.001085
114.00 13 0.001085
138.00 13 0.001085
69.72 13 0.001085
61.00 13 0.001085
4000.00 13 0.001085
1202.04 12 0.001001
732.00 12 0.001001
84.16 12 0.001001
195.00 12 0.001001
268.00 12 0.001001
2163.60 12 0.001001
58.00 12 0.001001
360.61 12 0.001001
210.35 12 0.001001
120.12 12 0.001001
756.00 12 0.001001
90.12 12 0.001001
7.20 12 0.001001
304.00 12 0.001001
30.12 11 0.000918
696.00 11 0.000918
122.00 11 0.000918
768.00 11 0.000918
292.00 10 0.000835
284.00 10 0.000835
198.00 10 0.000835
106.00 10 0.000835
1400.00 10 0.000835
1920.00 10 0.000835
410.00 10 0.000835
57.00 10 0.000835
580.00 10 0.000835
19.00 10 0.000835
344.00 10 0.000835
84.12 10 0.000835
316.00 10 0.000835
134.00 10 0.000835
255.00 9 0.000751
804.00 9 0.000751
81.00 9 0.000751
2100.00 9 0.000751
2040.00 9 0.000751
296.00 9 0.000751
66.11 9 0.000751
470.00 9 0.000751
7.50 9 0.000751
101.00 9 0.000751
142.00 9 0.000751
880.00 9 0.000751
1032.00 9 0.000751
1100.00 8 0.000668
47.00 8 0.000668
1380.00 8 0.000668
171.96 8 0.000668
29.00 8 0.000668
83.00 8 0.000668
202.00 8 0.000668
222.00 8 0.000668
115.36 8 0.000668
90.36 8 0.000668
7.21 8 0.000668
41.00 8 0.000668
888.00 7 0.000584
372.60 7 0.000584
336.56 7 0.000584
816.00 7 0.000584
5000.00 7 0.000584
50.40 7 0.000584
93.00 7 0.000584
123.00 7 0.000584
256.00 7 0.000584
430.00 7 0.000584
43.32 6 0.000501
154.00 6 0.000501
108.16 6 0.000501
28.80 6 0.000501
308.00 6 0.000501
1212.00 6 0.000501
850.00 6 0.000501
57.72 6 0.000501
820.00 6 0.000501
96.12 6 0.000501
182.00 6 0.000501
14.40 6 0.000501
332.00 6 0.000501
984.00 6 0.000501
50.52 6 0.000501
376.00 6 0.000501
920.00 6 0.000501
285.00 6 0.000501
588.00 6 0.000501
328.00 6 0.000501
43.00 6 0.000501
57.69 6 0.000501
852.00 5 0.000417
7212.12 5 0.000417
540.96 5 0.000417
305.00 5 0.000417
4200.00 5 0.000417
91.00 5 0.000417
14.44 5 0.000417
286.00 5 0.000417
1009.68 5 0.000417
721.22 5 0.000417
79.32 5 0.000417
99.96 5 0.000417
99.00 5 0.000417
1120.00 5 0.000417
1040.00 5 0.000417
97.00 5 0.000417
448.00 5 0.000417
368.00 5 0.000417
132.20 5 0.000417
40.05 5 0.000417
722.88 5 0.000417
416.00 5 0.000417
52.89 5 0.000417
151.00 5 0.000417
186.00 5 0.000417
252.36 5 0.000417
1620.00 5 0.000417
841.40 4 0.000334
18.04 4 0.000334
601.02 4 0.000334
30.06 4 0.000334
828.00 4 0.000334
876.00 4 0.000334
475.00 4 0.000334
510.00 4 0.000334
937.56 4 0.000334
59.00 4 0.000334
924.00 4 0.000334
149.00 4 0.000334
0.72 4 0.000334
300.52 4 0.000334
1.00 4 0.000334
1202.00 4 0.000334
1250.00 4 0.000334
450.75 4 0.000334
30.04 4 0.000334
49.00 4 0.000334
388.00 4 0.000334
118.00 4 0.000334
194.00 4 0.000334
740.00 4 0.000334
625.00 4 0.000334
1128.00 4 0.000334
240.36 4 0.000334
100.15 4 0.000334
346.08 4 0.000334
2884.92 4 0.000334
352.00 4 0.000334
45.07 4 0.000334
1464.00 4 0.000334
333.00 4 0.000334
424.00 4 0.000334
912.00 4 0.000334
345.00 4 0.000334
12.50 4 0.000334
245.00 4 0.000334
161.00 4 0.000334
201.00 4 0.000334
1298.16 3 0.000250
315.00 3 0.000250
60.01 3 0.000250
198.33 3 0.000250
93.72 3 0.000250
265.00 3 0.000250
306.00 3 0.000250
1300.00 3 0.000250
109.00 3 0.000250
396.60 3 0.000250
1081.84 3 0.000250
70.10 3 0.000250
35.05 3 0.000250
274.00 3 0.000250
488.00 3 0.000250
1860.00 3 0.000250
234.00 3 0.000250
1296.00 3 0.000250
1284.00 3 0.000250
708.00 3 0.000250
1355.88 3 0.000250
1224.00 3 0.000250
1730.88 3 0.000250
3606.00 3 0.000250
996.00 3 0.000250
262.00 3 0.000250
113.00 3 0.000250
901.52 3 0.000250
1980.00 3 0.000250
187.00 3 0.000250
4327.32 3 0.000250
103.00 3 0.000250
725.00 3 0.000250
43.20 3 0.000250
166.00 3 0.000250
71.00 3 0.000250
45.08 3 0.000250
18000.00 3 0.000250
21.64 3 0.000250
240.96 3 0.000250
2880.00 3 0.000250
8000.00 3 0.000250
72000.00 3 0.000250
436.00 3 0.000250
89.00 3 0.000250
111.00 3 0.000250
961.64 3 0.000250
324.60 3 0.000250
52.02 3 0.000250
54.12 3 0.000250
415.00 3 0.000250
137.00 3 0.000250
9.01 3 0.000250
7212.00 3 0.000250
87.96 3 0.000250
121.00 3 0.000250
64.92 3 0.000250
24000.00 3 0.000250
480.81 3 0.000250
2404.04 3 0.000250
186.32 3 0.000250
159.96 3 0.000250
2.00 3 0.000250
4320.00 2 0.000167
420.60 2 0.000167
39.96 2 0.000167
793.32 2 0.000167
3200.00 2 0.000167
294.00 2 0.000167
346.00 2 0.000167
99.60 2 0.000167
25.98 2 0.000167
36.66 2 0.000167
79.00 2 0.000167
446.00 2 0.000167
1.20 2 0.000167
94.96 2 0.000167
93.24 2 0.000167
540.60 2 0.000167
75.72 2 0.000167
2700.00 2 0.000167
43.27 2 0.000167
335.00 2 0.000167
1204.00 2 0.000167
8.67 2 0.000167
26.44 2 0.000167
525.00 2 0.000167
173.00 2 0.000167
530.00 2 0.000167
65.10 2 0.000167
122.64 2 0.000167
860.00 2 0.000167
2500.00 2 0.000167
356.00 2 0.000167
42.05 2 0.000167
59.08 2 0.000167
412.00 2 0.000167
2280.00 2 0.000167
120.01 2 0.000167
36000.00 2 0.000167
69.00 2 0.000167
240.60 2 0.000167
79.92 2 0.000167
223.92 2 0.000167
1104.00 2 0.000167
144.48 2 0.000167
153.00 2 0.000167
177.00 2 0.000167
157.00 2 0.000167
167.00 2 0.000167
189.00 2 0.000167
570.00 2 0.000167
127.00 2 0.000167
198.36 2 0.000167
240.12 2 0.000167
148.24 2 0.000167
5400.00 2 0.000167
60.05 2 0.000167
146.00 2 0.000167
211.56 2 0.000167
100.01 2 0.000167
123.96 2 0.000167
187.20 2 0.000167
158.00 2 0.000167
223.20 2 0.000167
55.92 2 0.000167
295.00 2 0.000167
458.00 2 0.000167
25.20 2 0.000167
585.00 2 0.000167
366.00 2 0.000167
51.60 2 0.000167
2520.00 2 0.000167
2340.00 2 0.000167
139.00 2 0.000167
2200.00 2 0.000167
1360.00 2 0.000167
961.60 2 0.000167
138.23 2 0.000167
1596.00 2 0.000167
1.80 2 0.000167
200.04 2 0.000167
237.96 2 0.000167
27.05 2 0.000167
282.00 2 0.000167
1202.02 2 0.000167
249.96 2 0.000167
2640.00 2 0.000167
420.71 2 0.000167
30.50 2 0.000167
90.16 2 0.000167
613.08 2 0.000167
159.40 2 0.000167
144.12 2 0.000167
1056.00 2 0.000167
126.21 2 0.000167
1803.04 2 0.000167
92.12 2 0.000167
810.00 2 0.000167
6.02 2 0.000167
302.00 2 0.000167
2524.20 2 0.000167
74.52 2 0.000167
77.10 1 0.000083
2760.00 1 0.000083
208.08 1 0.000083
80.40 1 0.000083
6600.00 1 0.000083
669.60 1 0.000083
40.06 1 0.000083
160.08 1 0.000083
2328.00 1 0.000083
558.00 1 0.000083
156.24 1 0.000083
216.72 1 0.000083
484.00 1 0.000083
382.00 1 0.000083
374.00 1 0.000083
343.92 1 0.000083
176.24 1 0.000083
139.36 1 0.000083
464.00 1 0.000083
630.00 1 0.000083
32.05 1 0.000083
1512.00 1 0.000083
115.32 1 0.000083
214.00 1 0.000083
385.00 1 0.000083
152.24 1 0.000083
10.50 1 0.000083
112.12 1 0.000083
289.00 1 0.000083
300000.00 1 0.000083
164.24 1 0.000083
140.20 1 0.000083
100.10 1 0.000083
264.24 1 0.000083
238.80 1 0.000083
74.40 1 0.000083
14.02 1 0.000083
2016.00 1 0.000083
79.20 1 0.000083
211.52 1 0.000083
278.00 1 0.000083
252.40 1 0.000083
720.80 1 0.000083
102.17 1 0.000083
128.20 1 0.000083
19.50 1 0.000083
199.20 1 0.000083
842.88 1 0.000083
4080.00 1 0.000083
224.24 1 0.000083
532.00 1 0.000083
1752.00 1 0.000083
2800.00 1 0.000083
245.16 1 0.000083
117.00 1 0.000083
3020.00 1 0.000083
824.56 1 0.000083
258.00 1 0.000083
168.24 1 0.000083
364.80 1 0.000083
243.96 1 0.000083
402.00 1 0.000083
1992.00 1 0.000083
90.14 1 0.000083
206.00 1 0.000083
306.51 1 0.000083
59.88 1 0.000083
35.88 1 0.000083
211.00 1 0.000083
253.00 1 0.000083
300.48 1 0.000083
1280.00 1 0.000083
1092.00 1 0.000083
181.00 1 0.000083
468.12 1 0.000083
4500.00 1 0.000083
66.10 1 0.000083
968.00 1 0.000083
973.20 1 0.000083
280.40 1 0.000083
1442.43 1 0.000083
428.00 1 0.000083
536.00 1 0.000083
318.00 1 0.000083
544.00 1 0.000083
135.96 1 0.000083
126.84 1 0.000083
218.00 1 0.000083
160.25 1 0.000083
338.00 1 0.000083
322.00 1 0.000083
63.60 1 0.000083
1160.00 1 0.000083
1116.00 1 0.000083
1117.80 1 0.000083
505.00 1 0.000083
144.20 1 0.000083
972.00 1 0.000083
1044.00 1 0.000083
38.40 1 0.000083
812.00 1 0.000083
1750.00 1 0.000083
212.76 1 0.000083
901.20 1 0.000083
160.20 1 0.000083
210.32 1 0.000083
323.00 1 0.000083
641.00 1 0.000083
1356.00 1 0.000083
3800.00 1 0.000083
36.05 1 0.000083
798.00 1 0.000083
302.88 1 0.000083
320.50 1 0.000083
260.40 1 0.000083
728.00 1 0.000083
164.40 1 0.000083
451.00 1 0.000083
273.00 1 0.000083
572.00 1 0.000083
110.40 1 0.000083
126.15 1 0.000083
2884.80 1 0.000083
692.28 1 0.000083
311.00 1 0.000083
188.04 1 0.000083
281.00 1 0.000083
1584.00 1 0.000083
100.92 1 0.000083
382.08 1 0.000083
76.12 1 0.000083
60.15 1 0.000083
480.24 1 0.000083
1502.40 1 0.000083
1908.00 1 0.000083
480.60 1 0.000083
9840.00 1 0.000083
3360.00 1 0.000083
40.20 1 0.000083
133.32 1 0.000083
592.00 1 0.000083
207.00 1 0.000083
741.00 1 0.000083
3300.00 1 0.000083
70.01 1 0.000083
247.68 1 0.000083
1644.00 1 0.000083
55.56 1 0.000083
169.00 1 0.000083
120.10 1 0.000083
199.00 1 0.000083
3180.00 1 0.000083
980.00 1 0.000083
158.64 1 0.000083
1803.03 1 0.000083
608.00 1 0.000083
340.08 1 0.000083
438.00 1 0.000083
466.64 1 0.000083
901.00 1 0.000083
107.00 1 0.000083
17.34 1 0.000083
565.00 1 0.000083
139.92 1 0.000083
30.40 1 0.000083
147.00 1 0.000083
675.00 1 0.000083
409.44 1 0.000083
238.00 1 0.000083
8400.00 1 0.000083
90.75 1 0.000083
54.08 1 0.000083
1226.04 1 0.000083
66.66 1 0.000083
468.84 1 0.000083
51.09 1 0.000083
54.93 1 0.000083
9.32 1 0.000083
245.20 1 0.000083
143.04 1 0.000083
9.60 1 0.000083
54.60 1 0.000083
18030.36 1 0.000083
70.12 1 0.000083
165.84 1 0.000083
1153.92 1 0.000083
69.96 1 0.000083
42.08 1 0.000083
3137.28 1 0.000083
123.80 1 0.000083
219.36 1 0.000083
1478.52 1 0.000083
75.13 1 0.000083
197.52 1 0.000083
162.24 1 0.000083
99.12 1 0.000083
1586.64 1 0.000083
512.08 1 0.000083
8654.64 1 0.000083
6010.12 1 0.000083
594.96 1 0.000083
175.32 1 0.000083
105.24 1 0.000083
2352.00 1 0.000083
451.96 1 0.000083
201.96 1 0.000083
450.76 1 0.000083
336.36 1 0.000083
183.48 1 0.000083
129.84 1 0.000083
230.76 1 0.000083
634.68 1 0.000083
46.92 1 0.000083
86.40 1 0.000083
447.12 1 0.000083
1442.44 1 0.000083
270.46 1 0.000083
31.24 1 0.000083
7.22 1 0.000083
259.48 1 0.000083
80.16 1 0.000083
3606.24 1 0.000083
29.99 1 0.000083
468.72 1 0.000083
156.28 1 0.000083
16.84 1 0.000083
2957.04 1 0.000083
115.44 1 0.000083
1139.52 1 0.000083
73.20 1 0.000083
492.84 1 0.000083
100.96 1 0.000083
175.25 1 0.000083
150.24 1 0.000083
1033.76 1 0.000083
690.00 1 0.000083
36.48 1 0.000083
7224.00 1 0.000083
0.60 1 0.000083
1201.80 1 0.000083
183.00 1 0.000083
24156.00 1 0.000083
120.36 1 0.000083
999.96 1 0.000083
36.07 1 0.000083
2199.72 1 0.000083
151.44 1 0.000083
292.80 1 0.000083
242.50 1 0.000083
1002.00 1 0.000083
405.96 1 0.000083
34.64 1 0.000083
7260.00 1 0.000083
24.02 1 0.000083
295.68 1 0.000083
60.60 1 0.000083
300.84 1 0.000083
125.34 1 0.000083
217.08 1 0.000083
128.02 1 0.000083
144.60 1 0.000083
2253.80 1 0.000083
291.96 1 0.000083
132.22 1 0.000083
7596.00 1 0.000083
193.00 1 0.000083
264.44 1 0.000083
5071.76 1 0.000083
405.00 1 0.000083
277.00 1 0.000083
4.80 1 0.000083
201.60 1 0.000083
2004.00 1 0.000083
362.00 1 0.000083
138.15 1 0.000083
1536.00 1 0.000083
18.06 1 0.000083
1872.00 1 0.000083
120000.00 1 0.000083
25.60 1 0.000083
64.80 1 0.000083
44.84 1 0.000083
7992.00 1 0.000083
2184.00 1 0.000083
241.00 1 0.000083
3480.00 1 0.000083
171.00 1 0.000083
280.36 1 0.000083
108.36 1 0.000083
67.20 1 0.000083
86.56 1 0.000083
145.20 1 0.000083
1.50 1 0.000083
120.08 1 0.000083
102.72 1 0.000083
632.00 1 0.000083
131.00 1 0.000083
100.20 1 0.000083
388.92 1 0.000083
36060.00 1 0.000083
20580.00 1 0.000083
209.00 1 0.000083
384.64 1 0.000083
59.02 1 0.000083
3612.00 1 0.000083
360.24 1 0.000083
38.85 1 0.000083
9600.00 1 0.000083
10404.00 1 0.000083
515.28 1 0.000083
119.88 1 0.000083
519.60 1 0.000083
3096.00 1 0.000083
1812.00 1 0.000083
1084.32 1 0.000083
24456.00 1 0.000083
9000.00 1 0.000083
33.60 1 0.000083
10800.00 1 0.000083
25140.00 1 0.000083
89.25 1 0.000083
83.33 1 0.000083
90.96 1 0.000083
2064.00 1 0.000083
143.00 1 0.000083
71.96 1 0.000083
209.16 1 0.000083
796.00 1 0.000083
590.00 1 0.000083
225.35 1 0.000083
556.00 1 0.000083
65.86 1 0.000083
550.75 1 0.000083
360.12 1 0.000083
59.50 1 0.000083
90.10 1 0.000083
43.28 1 0.000083
50.48 1 0.000083
824.00 1 0.000083
64.90 1 0.000083
930.00 1 0.000083
8166.00 1 0.000083
150.15 1 0.000083
623.52 1 0.000083
10080.00 1 0.000083
242.00 1 0.000083
1524.00 1 0.000083
189.36 1 0.000083
16.80 1 0.000083
76.20 1 0.000083
558.96 1 0.000083
333.60 1 0.000083
710.00 1 0.000083
79992.00 1 0.000083
193.92 1 0.000083
2282.40 1 0.000083
15.52 1 0.000083
594.48 1 0.000083
105.60 1 0.000083
10.80 1 0.000083
108.96 1 0.000083
330.56 1 0.000083
166.56 1 0.000083
80.04 1 0.000083
9.24 1 0.000083
30.03 1 0.000083
92.32 1 0.000083
1692.00 1 0.000083
219.60 1 0.000083
95.05 1 0.000083
376.80 1 0.000083
399.60 1 0.000083
112.99 1 0.000083
1211.64 1 0.000083
15308.88 1 0.000083
72.20 1 0.000083
52.88 1 0.000083
5.87 1 0.000083
30050.60 1 0.000083
386.00 1 0.000083
66660.00 1 0.000083
2000000.00 1 0.000083
333.76 1 0.000083
141.00 1 0.000083
21636.00 1 0.000083
72.30 1 0.000083
288.49 1 0.000083
1060.00 1 0.000083
1728.00 1 0.000083
360.72 1 0.000083
462.00 1 0.000083
9015.24 1 0.000083
15000.00 1 0.000083
1146.72 1 0.000083
300.01 1 0.000083
1502.53 1 0.000083
1476.00 1 0.000083
36420.00 1 0.000083
624.24 1 0.000083
14400.00 1 0.000083
411.96 1 0.000083
195.96 1 0.000083
252.43 1 0.000083
143.64 1 0.000083
1150.00 1 0.000083
26.36 1 0.000083
13.82 1 0.000083
36.80 1 0.000083
68.43 1 0.000083
132.12 1 0.000083
msf_annualizedquota__c: Aportación del socio anualizada.
No hay vacios ni nulos, se acumula casi el 50% de la poblacion entre los 60 y los 180€.
Analsis de distribución por variables
-> msf_cancelationdate__c: Variable fecha
In [44]:
# Vamos a realizar analisis por cada variable
var = "msf_cancelationdate__c"
In [45]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable msf_cancelationdate__c es 483006. Lo que supone un 40.3107309504952%
El nº de vacios para la variable msf_cancelationdate__c es 0. Lo que supone un 0.0%
Out[45]:
['isdeleted', 'msf_cancelationdate__c']
In [46]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[46]:
# Tot % Tot
2014-03-13 2887 0.403663
2020-03-12 2637 0.368708
2018-03-07 2198 0.307326
2018-04-09 1957 0.273629
2023-05-10 1867 0.261045
... ... ...
2002-08-07 1 0.000140
2012-06-23 1 0.000140
2008-03-21 1 0.000140
2007-02-17 1 0.000140
2002-07-04 1 0.000140

7160 rows × 2 columns

msf_cancelationdate__c: Fecha de cancelación de la aportación.
Exite un 40% de vacios, que son las donaciones actualmente en vigor.
Analsis de distribución por variables
-> msf_cancelationreason__c: Variable alfanumerica
In [47]:
# Vamos a realizar analisis por cada variable
var = "msf_cancelationreason__c"
In [48]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable msf_cancelationreason__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_cancelationreason__c es 483501. Lo que supone un 40.35204267710004%
Out[48]:
['isdeleted', 'msf_cancelationdate__c', 'msf_cancelationreason__c']
In [49]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[49]:
# Tot % Tot
483501 40.352043
Impago aportaciones 285429 23.821343
Unpaid 102935 8.590753
Baja económica argumentada 66594 5.557804
3 Obs/Tcs Devueltas 66286 5.532099
Económico 39385 3.286995
N/S 35770 2.985294
Razones personales 34347 2.866533
Voluntary withdrawal 16894 1.409940
Deceased 15218 1.270064
Otras razones 8112 0.677012
2 OBS devueltas es la primera de su historia 5346 0.446167
Baja argumentada 4854 0.405105
Baja por cambio de titular 3394 0.283257
Cambio de domicilio 3241 0.270487
Colabora con otra ONG 3065 0.255799
Baja e llamada de Bienvenida 2933 0.244782
Baja por impago orden Socio 2832 0.236353
1 OBS/TCS devueltas con otras aportaciones impagadas en los últimos 2 años 2711 0.226255
Baja en llamada de gestión de devoluciones 1827 0.152478
Tarjeta crédito caducada 1799 0.150141
Baja proactiva Coronavirus 1711 0.142797
Decepcionado 1402 0.117008
Baja en llamada de aumentos 1328 0.110832
Errores administrativos MSF 1277 0.106576
Incidencias Web 906 0.075613
Cierre/Cambio cuenta bancaria 767 0.064012
Pruebas 650 0.054248
Impago Coronavirus 464 0.038725
LOPD 449 0.037473
Desacuerdo política intervención 439 0.036638
Captador F2F-TLMK me informó mal 341 0.028459
Baja socio temporal 323 0.026957
Error del proveedor 307 0.025622
Desacuerdo aborto 252 0.021031
Duplicado 238 0.019863
Desacuerdo política captacion de fondos 229 0.019112
Baja reactiva Coronavirus 214 0.017860
Desacuerdo operaciones mediterraneo 142 0.011851
2 TCS devueltas 116 0.009681
Desacuerdo gestión financiera 85 0.007094
Desacuerdo destino ayuda 59 0.004924
Desacuerdo colaboración con Empresas 26 0.002170
LORTAD 9 0.000751
msf_cancelationreason__c: Motivo de cancelación de la aportación.
Exite un 40% de vacios, que son las donaciones actualmente en vigor.
Analsis de distribución por variables
-> msf_currentcampaign__c: Variable alfanumerica
In [50]:
# Vamos a realizar analisis por cada variable
var = "msf_currentcampaign__c"
In [51]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable msf_currentcampaign__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_currentcampaign__c es 19006. Lo que supone un 1.5862033855585889%
In [52]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[52]:
# Tot % Tot
7013Y000001mqtMQAQ 95523 7.972162
7013Y000001mqtnQAA 47771 3.986874
7013Y000001vXGdQAM 30352 2.533118
7013Y000001mrBLQAY 30029 2.506161
7013Y000001mrCzQAI 28901 2.412021
... ... ...
7013Y000001mrkDQAQ 1 0.000083
7013Y000001mrGNQAY 1 0.000083
7013Y000001mrO8QAI 1 0.000083
7013Y000001Mc2FQAS 1 0.000083
7013Y000001mr7eQAA 1 0.000083

2600 rows × 2 columns

msf_currentcampaign__c: Campaña actual de la aportación.
Exite un 1,5% de vacios. Hay muchisima distribución entre los diferentes id de campaña.
Analsis de distribución por variables
-> msf_currentleadsource1__c: Variable alfanumerica
In [53]:
# Vamos a realizar analisis por cada variable
var = "msf_currentleadsource1__c"
In [54]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable msf_currentleadsource1__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_currentleadsource1__c es 19170. Lo que supone un 1.5998905030599888%
In [55]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[55]:
# Tot % Tot
Telemarketing 509871 42.552831
Persona a persona 255845 21.352321
Cupón 84961 7.090678
Otro 71618 5.977097
Personal con tablet 64333 5.369106
Web MSF 63379 5.289487
Teléfono campaña 48761 4.069497
Teléfono SAS 21594 1.802193
Web campaña 20290 1.693364
19170 1.599891
Entidad financiera 8710 0.726919
Email a SAS 8083 0.674591
Web terceros 7062 0.589381
Teléfono web 7001 0.584290
Correo postal sin cupón 4185 0.349272
Web MSF Mi perfil 1057 0.088215
Cloud page 885 0.073860
Eventos 763 0.063678
Teléfono Officers 389 0.032465
Email a Empresas 182 0.015189
Email a officers Mid Donors 22 0.001836
SMS 15 0.001252
Email a One to one 8 0.000668
n/a 7 0.000584
Email a Bodas 6 0.000501
Teléfono Herencias y Legados 5 0.000417
Email a Iniciativas Solidarias 2 0.000167
Email Director/a General 2 0.000167
Redes Sociales 1 0.000083
msf_currentleadsource1__c: Canal actual de la donación.
Exite un 1,5% de vacios. La mayoria es telemarketing o persona a persona.
Analsis de distribución por variables
-> msf_leadsource1__c: Variable alfanumerica
In [56]:
# Vamos a realizar analisis por cada variable
var = "msf_leadsource1__c"
In [57]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable msf_leadsource1__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_leadsource1__c es 173. Lo que supone un 0.014438239803306108%
In [58]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[58]:
# Tot % Tot
Persona a persona 374964 31.293758
Telemarketing 289168 24.133393
Otro 136249 11.371074
Cupón 133197 11.116360
Web MSF 76674 6.399061
Personal con tablet 74059 6.180819
Teléfono campaña 61869 5.163465
Web campaña 14210 1.185939
Web terceros 12698 1.059750
Teléfono SAS 6595 0.550406
Teléfono web 5748 0.479717
Email a SAS 5354 0.446834
Correo postal sin cupón 5008 0.417958
Eventos 1577 0.131613
Entidad financiera 343 0.028626
173 0.014438
Email a Empresas 162 0.013520
Teléfono Officers 126 0.010516
Email a officers Mid Donors 9 0.000751
Cloud page 8 0.000668
Email a One to one 5 0.000417
n/a 3 0.000250
SMS 2 0.000167
Teléfono Herencias y Legados 2 0.000167
Email a Iniciativas Solidarias 2 0.000167
Redes Sociales 1 0.000083
Email Director/a General 1 0.000083
msf_leadsource1__c: Canal de captura de la donación.
Exite menos del 1% de vacios. La mayoria es telemarketing o persona a persona.
Analsis de distribución por variables
-> npe03__amount__c: Variable numerica
In [59]:
# Vamos a realizar analisis por cada variable
var = "npe03__amount__c"
In [60]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npe03__amount__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npe03__amount__c es 0. Lo que supone un 0.0%
In [61]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[61]:
# Tot % Tot
10.00 283542 23.663858
15.00 133048 11.103924
5.00 113650 9.485006
20.00 89248 7.448463
12.00 62605 5.224890
30.00 50617 4.224395
6.00 48472 4.045378
3.00 30398 2.536957
25.00 29725 2.480790
50.00 24828 2.072096
8.00 22023 1.837996
60.00 19257 1.607151
0.00 19245 1.606150
7.00 16418 1.370214
6.01 16086 1.342506
100.00 14742 1.230338
14.00 13508 1.127351
40.00 12300 1.026534
30.05 12251 1.022444
18.03 11212 0.935731
18.00 8598 0.717572
17.00 7785 0.649721
60.10 7704 0.642961
4.33 6937 0.578948
35.00 6936 0.578865
9.00 6883 0.574442
150.00 6295 0.525368
4.00 6150 0.513267
120.00 6065 0.506173
11.00 5674 0.473541
16.00 5324 0.444331
13.00 5227 0.436235
22.00 4576 0.381904
12.02 4428 0.369552
26.00 4239 0.353779
45.00 4037 0.336920
200.00 3959 0.330410
36.00 3453 0.288181
19.00 3367 0.281003
70.00 3218 0.268568
75.00 2988 0.249373
80.00 2808 0.234350
90.00 2800 0.233682
23.00 2715 0.226589
24.00 2615 0.218243
300.00 1999 0.166833
32.00 1809 0.150976
21.00 1628 0.135870
90.15 1614 0.134701
28.00 1449 0.120931
250.00 1391 0.116090
65.00 1357 0.113253
55.00 1308 0.109163
27.00 1303 0.108746
72.12 1253 0.104573
42.00 1185 0.098898
33.00 1124 0.093807
34.86 1123 0.093723
72.00 1114 0.092972
180.00 1069 0.089217
125.00 919 0.076698
130.00 910 0.075947
36.06 865 0.072191
110.00 813 0.067851
140.00 711 0.059339
120.20 653 0.054498
150.25 636 0.053079
500.00 601 0.050158
9.02 594 0.049574
14.42 586 0.048906
48.00 577 0.048155
160.00 557 0.046486
52.00 555 0.046319
3.01 551 0.045985
38.00 493 0.041145
85.00 489 0.040811
34.00 485 0.040477
400.00 456 0.038057
240.00 441 0.036805
37.00 440 0.036722
24.04 424 0.035386
7.50 416 0.034719
15.03 408 0.034051
48.08 397 0.033133
84.00 383 0.031964
170.00 379 0.031631
31.00 376 0.031380
8.67 357 0.029795
175.00 355 0.029628
260.00 335 0.027958
350.00 329 0.027458
600.00 322 0.026873
1000.00 313 0.026122
210.00 283 0.023619
28.85 271 0.022617
29.00 269 0.022450
165.00 260 0.021699
105.00 230 0.019195
115.00 227 0.018945
220.00 226 0.018862
46.00 219 0.018277
93.16 218 0.018194
62.00 203 0.016942
180.30 202 0.016859
12.50 201 0.016775
230.00 196 0.016358
56.00 189 0.015774
8.66 187 0.015607
78.00 186 0.015523
6.02 184 0.015356
135.00 184 0.015356
43.00 183 0.015273
144.00 181 0.015106
39.00 180 0.015022
44.00 179 0.014939
95.00 175 0.014605
54.00 174 0.014522
42.07 155 0.012936
41.00 153 0.012769
300.51 150 0.012519
34.85 150 0.012519
225.00 144 0.012018
66.00 144 0.012018
360.00 142 0.011851
190.00 138 0.011517
54.09 126 0.010516
96.00 124 0.010349
58.00 115 0.009598
155.00 114 0.009514
450.00 109 0.009097
9.01 106 0.008847
63.00 95 0.007929
2.00 95 0.007929
270.00 87 0.007261
57.70 87 0.007261
15.02 86 0.007177
47.00 83 0.006927
53.00 79 0.006593
51.00 77 0.006426
84.14 76 0.006343
320.00 73 0.006092
275.00 70 0.005842
601.01 65 0.005425
30.12 65 0.005425
144.24 65 0.005425
145.00 65 0.005425
280.00 62 0.005174
68.00 61 0.005091
310.00 60 0.005007
74.00 59 0.004924
98.00 59 0.004924
700.00 57 0.004757
57.00 56 0.004674
390.00 56 0.004674
300.50 55 0.004590
82.00 54 0.004507
330.00 54 0.004507
4.50 53 0.004423
1.00 51 0.004256
67.00 49 0.004089
112.00 47 0.003923
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173.00 2 0.000167
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npe03__amount__c: Importe de la donación.
No exiten vacios, hay mucha distribución entre diferentes importes.
Analsis de distribución por variables
-> npe03__date_established__c: Variable fecha
In [62]:
# Vamos a realizar analisis por cada variable
var = "npe03__date_established__c"
In [63]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npe03__date_established__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npe03__date_established__c es 0. Lo que supone un 0.0%
In [64]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[64]:
# Tot % Tot
2004-01-01 5000 0.417290
2000-02-01 4604 0.384241
1994-10-01 3829 0.319561
2000-01-01 3810 0.317975
1995-02-01 3377 0.281838
... ... ...
2000-09-08 1 0.000083
1991-02-16 1 0.000083
1993-11-08 1 0.000083
1992-09-30 1 0.000083
2003-05-06 1 0.000083

7944 rows × 2 columns

npe03__date_established__c: Fecha de alta de la donación recurrente.
No exiten vacios, hay mucha distribución, se puede utilizar para la generacion de una variable de "meses_desde_inicio", considerando que alguno de ellos ha cancelado.
Analsis de distribución por variables
-> npe03__installment_period__c: Variable numerica
In [65]:
# Vamos a realizar analisis por cada variable
var = "npe03__installment_period__c"
In [66]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npe03__installment_period__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npe03__installment_period__c es 28832. Lo que supone un 2.4062620231729577%
In [67]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[67]:
# Tot % Tot
Monthly 928070 77.454897
Yearly 125456 10.470311
Quarterly 89401 7.461232
28832 2.406262
Semestral 20047 1.673083
Bimensual 6401 0.534215
npe03__installment_period__c: Periodicidad de las aportaciones.
Exite un 2% de vacios. Lo más comun es tener cuota mensual.
Analsis de distribución por variables
-> npe03__open_ended_status__c: Variable numerica
In [68]:
# Vamos a realizar analisis por cada variable
var = "npe03__open_ended_status__c"
In [69]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npe03__open_ended_status__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npe03__open_ended_status__c es 0. Lo que supone un 0.0%
In [70]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[70]:
# Tot % Tot
Closed 715235 59.692107
Open 482947 40.305807
None 24 0.002003
Close 1 0.000083
npe03__open_ended_status__c: Indicador de donacion recurrente cancelada.
Exiten unos pocos registros erroneos, pero la variable es util para saber quienes son los activos a dia de hoy que serán el objetivo y los cancelados podran ser utilizados como datos historicos en el modelo pero a dia de hoy no es objetivo.
Analsis de distribución por variables
-> npe03__paid_amount__c: Variable numerica
In [71]:
# Vamos a realizar analisis por cada variable
var = "npe03__paid_amount__c"
In [72]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npe03__paid_amount__c es 1899. Lo que supone un 0.15848680570218668%
El nº de vacios para la variable npe03__paid_amount__c es 0. Lo que supone un 0.0%
In [73]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[73]:
# Tot % Tot
0.00 465514 38.912554
670.00 40774 3.408320
1005.00 22332 1.866743
1340.00 14305 1.195762
335.00 11733 0.980767
... ... ...
659.53 1 0.000084
220.04 1 0.000084
277.32 1 0.000084
384.24 1 0.000084
970.45 1 0.000084

7095 rows × 2 columns

npe03__paid_amount__c: Importe acumulado de todas las donaciones hasta el momento.
Existen algunos nulos, hay mucha distribución entre diferentes importes.
Analsis de distribución por variables
-> npe03__recurring_donation_campaign__c: Variable alfanumerica
In [74]:
# Vamos a realizar analisis por cada variable
var = "npe03__recurring_donation_campaign__c"
In [75]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npe03__recurring_donation_campaign__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npe03__recurring_donation_campaign__c es 1. Lo que supone un 8.3458033545122e-05%
In [76]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[76]:
# Tot % Tot
7013Y000001mqtnQAA 55336 4.618234
7013Y000001mr4CQAQ 38368 3.202118
7013Y000001mrCzQAI 35953 3.000567
7013Y000001mr2DQAQ 32217 2.688767
7013Y000001mr2cQAA 27436 2.289755
... ... ...
7013Y000001mrZJQAY 1 0.000083
7013Y000001mrgaQAA 1 0.000083
7013Y000001mrHlQAI 1 0.000083
7013Y000001vCNjQAM 1 0.000083
7013Y000001mrKFQAY 1 0.000083

2746 rows × 2 columns

npe03__recurring_donation_campaign__c: Campaña de camptura de la aportación.
Exite muy pocos vacios. Hay muchisima distribución entre los diferentes id de campaña.
Analsis de distribución por variables
-> npe03__total_paid_installments__c: Variable numerica
In [77]:
# Vamos a realizar analisis por cada variable
var = "npe03__total_paid_installments__c"
In [78]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npe03__total_paid_installments__c es 1899. Lo que supone un 0.15848680570218668%
El nº de vacios para la variable npe03__total_paid_installments__c es 0. Lo que supone un 0.0%
In [79]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[79]:
# Tot % Tot
0.0 465514 38.912554
67.0 240168 20.075766
6.0 45376 3.793003
1.0 34577 2.890309
5.0 32738 2.736586
2.0 26558 2.219997
22.0 22576 1.887139
3.0 20987 1.754314
4.0 18250 1.525527
23.0 17528 1.465175
11.0 11887 0.993640
7.0 10423 0.871264
8.0 10076 0.842258
12.0 9527 0.796367
9.0 9273 0.775135
10.0 8725 0.729327
13.0 8149 0.681179
16.0 6981 0.583545
14.0 6895 0.576357
20.0 6394 0.534478
19.0 6183 0.516840
15.0 6163 0.515168
21.0 6139 0.513162
17.0 5729 0.478890
18.0 5632 0.470782
33.0 5537 0.462841
25.0 4887 0.408507
24.0 4797 0.400984
27.0 4743 0.396470
34.0 4673 0.390618
26.0 4623 0.386439
66.0 4569 0.381925
28.0 4163 0.347987
31.0 4105 0.343139
29.0 4099 0.342638
36.0 4006 0.334864
30.0 3949 0.330099
42.0 3881 0.324415
32.0 3729 0.311709
45.0 3719 0.310873
37.0 3706 0.309786
43.0 3573 0.298669
41.0 3531 0.295158
39.0 3505 0.292985
44.0 3414 0.285378
65.0 3406 0.284709
38.0 3376 0.282202
48.0 3373 0.281951
57.0 3286 0.274678
60.0 3282 0.274344
61.0 3236 0.270499
40.0 3194 0.266988
46.0 3189 0.266570
62.0 3154 0.263644
49.0 3144 0.262809
56.0 3097 0.258880
55.0 3076 0.257124
35.0 3033 0.253530
63.0 2998 0.250604
50.0 2997 0.250521
51.0 2994 0.250270
54.0 2966 0.247929
58.0 2847 0.237982
64.0 2830 0.236561
52.0 2820 0.235725
53.0 2740 0.229038
59.0 2635 0.220261
47.0 2622 0.219174
68.0 296 0.024743
69.0 29 0.002424
72.0 5 0.000418
70.0 3 0.000251
73.0 3 0.000251
75.0 3 0.000251
84.0 3 0.000251
78.0 2 0.000167
110.0 1 0.000084
159.0 1 0.000084
82.0 1 0.000084
74.0 1 0.000084
126.0 1 0.000084
141.0 1 0.000084
124.0 1 0.000084
88.0 1 0.000084
90.0 1 0.000084
123.0 1 0.000084
121.0 1 0.000084
93.0 1 0.000084
npe03__total_paid_installments__c: Numero de cuotas pagadas.
Exite muy pocos vacios, pero hay un 39% con 0 cuotas.
Analsis de distribución por variables
-> npsp4hub__payment_method__c: Variable alfanumerica
In [80]:
# Vamos a realizar analisis por cada variable
var = "npsp4hub__payment_method__c"
In [81]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npsp4hub__payment_method__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npsp4hub__payment_method__c es 19192. Lo que supone un 1.6017265797979814%
In [82]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[82]:
# Tot % Tot
Direct Debit 1172815 97.880834
19192 1.601727
CreditCard 6124 0.511097
ACMA 76 0.006343
npsp4hub__payment_method__c: Forma de pago de la aportación.
Exite muy pocos vacios. Pero practicamente todas las aportaciones con con debito directo, por lo que la variab le no aporta valor y se eliminará.
In [83]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_re_donation.append(var)
col_to_delete_re_donation
Out[83]:
['isdeleted',
 'msf_cancelationdate__c',
 'msf_cancelationreason__c',
 'npsp4hub__payment_method__c']

2. Tabla modificacion de cuota¶

In [84]:
# Vamos a analizar la tabla recurring donation
df = df_mod_cuota
In [85]:
# Se crea una lista por ahora vacia, en la que se irán añadiendo las variables que se van a eliminar del dataset por motivos varios: no utilidad, gran volumen de nulos, ...
col_to_delete_mod_cuota=list()
Analsis de distribución por variables
-> name: Variable string
In [86]:
# Vamos a realizar analisis por cada variable
var = "name"
In [87]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable name es 0. Lo que supone un 0.0%
El nº de vacios para la variable name es 0. Lo que supone un 0.0%
In [88]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[88]:
# Tot % Tot
I - 1606380943266 4 0.00020
I - 1607861743589 4 0.00020
I - 1607861743594 4 0.00020
I - 1604071257275 4 0.00020
I - 1606811457416 4 0.00020
... ... ...
I - 1582731654437693325 1 0.00005
A - 1582731654437693317 1 0.00005
A - 1582731654437693309 1 0.00005
A - 1582731654437693301 1 0.00005
D - 1688925499163 1 0.00005

1994102 rows × 2 columns

name: Codigo de la cuota modificada.
No hay vacios. Se puede extraer la primera letra de cada nombre para tener un indicador de si se ha modificado la cuota o no, y si se ha hecho ha sido incremento o decremento. Pero esta informacion ya está en otra variable (msf_changetype__c) por lo que no aporta valor y se eliminará. No olvidarnos de que el objetivo del analisis es el aumento de cuotas, por lo que la muestra representativa para el modelo será la que haya tenido modificaciones incrementales.
In [89]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_mod_cuota.append(var)
col_to_delete_mod_cuota
Out[89]:
['name']
Analsis de distribución por variables
-> msf_changeamount__c: Variable numerica
In [90]:
# Vamos a realizar analisis por cada variable
var = "msf_changeamount__c"
In [91]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_changeamount__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_changeamount__c es 0. Lo que supone un 0.0%
In [92]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[92]:
# Tot % Tot
1.000000e+01 407031 20.320876
5.000000e+00 294167 14.686181
1.500000e+01 152100 7.593538
2.000000e+00 133031 6.641525
3.000000e+00 121177 6.049718
... ... ...
1.237500e+03 1 0.000050
2.480000e+03 1 0.000050
1.343000e+02 1 0.000050
1.156700e+02 1 0.000050
6.507972e+08 1 0.000050

2597 rows × 2 columns

msf_changeamount__c: Importe del cambio de cuota.
No hay vacios. sobre el 35% es de 5 a 10€ de cambio.
Analsis de distribución por variables
-> msf_changeannualizedquota__c: Variable numerica
In [93]:
# Vamos a realizar analisis por cada variable
var = "msf_changeannualizedquota__c"
In [94]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_changeannualizedquota__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_changeannualizedquota__c es 0. Lo que supone un 0.0%
In [95]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[95]:
# Tot % Tot
1.200000e+02 385981 19.269962
6.000000e+01 301818 15.068155
2.400000e+01 133909 6.685358
1.800000e+02 132844 6.632189
3.600000e+01 119288 5.955410
... ... ...
1.389000e+01 1 0.000050
6.490920e+03 1 0.000050
2.939900e+02 1 0.000050
2.170800e+02 1 0.000050
7.809566e+09 1 0.000050

3658 rows × 2 columns

msf_changeannualizedquota__c: Importe de la modificacion en terminos anuales.
No hay vacios.Quizá es más interesante que la mensual, ya que el otro es por cuota que no todos tienen la misma periodicidad, en cambio esta es comparable para todos los registros. Por lo que no se va a tener en cuenta la anterior msf_changeamount__c.
In [96]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_mod_cuota.append("msf_changeamount__c")
col_to_delete_mod_cuota
Out[96]:
['name', 'msf_changeamount__c']
Analsis de distribución por variables
-> msf_changetype__c: Variable string
In [97]:
# Vamos a realizar analisis por cada variable
var = "msf_changetype__c"
In [98]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_changetype__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_changetype__c es 0. Lo que supone un 0.0%
In [99]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[99]:
# Tot % Tot
Initial 1174130 58.618016
Increase 767916 38.337929
Decrease 60927 3.041758
Changes_without_variation_annualized_fee 46 0.002297
col_to_delete_mod_cuota: Clase de modificación.
No hay vacios. Como el objetivo del analisis es el aumento de cuotas, por lo que la muestra representativa para el modelo será la que haya tenido modificaciones incrementales. Por lo que a la hora de crear el dataset final, esta variable nos servirá para crear una variable booleana que determinará como 1 a los que hayan incrementado y 0 al resto. Siendo esta variable a crear la variable objetivo del modelo.
Analsis de distribución por variables
-> msf_leadsource1__c: Variable string
In [100]:
# Vamos a realizar analisis por cada variable
var = "msf_leadsource1__c"
In [101]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_leadsource1__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_leadsource1__c es 324788. Lo que supone un 16.21492357286676%
In [102]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[102]:
# Tot % Tot
Telemarketing 832085 41.541543
Persona a persona 371059 18.524987
324788 16.214924
Web MSF 105789 5.281478
Cupón 86079 4.297463
Teléfono campaña 80095 3.998714
Personal con tablet 74862 3.737458
Entidad financiera 67438 3.366818
Teléfono SAS 17534 0.875379
Teléfono web 12659 0.631996
Web terceros 12565 0.627303
Email a SAS 10538 0.526106
Web campaña 4192 0.209284
Web MSF Mi perfil 939 0.046879
Cloud page 922 0.046031
Otro 542 0.027059
Teléfono Officers 441 0.022017
Correo postal sin cupón 227 0.011333
Email a Empresas 207 0.010334
Email a officers Mid Donors 23 0.001148
Email a One to one 10 0.000499
Email a Bodas 8 0.000399
Teléfono Herencias y Legados 6 0.000300
Email a Iniciativas Solidarias 4 0.000200
n/a 3 0.000150
SMS 2 0.000100
Email Director/a General 1 0.000050
Redes Sociales 1 0.000050
msf_leadsource1__c: Canal de modificación.
Hay un 16% de vacios. La distribucion se concentra en telemarketing.
Analsis de distribución por variables
-> msf_leadsource2__c: Variable string
In [103]:
# Vamos a realizar analisis por cada variable
var = "msf_leadsource2__c"
In [104]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_leadsource2__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_leadsource2__c es 324964. Lo que supone un 16.22371030928813%
In [105]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[105]:
# Tot % Tot
Telemarketing 832029 41.538747
Persona a persona 445916 22.262195
324964 16.223710
Formulario web 124405 6.210875
Cupón 86069 4.296964
Teléfono campaña 80020 3.994970
Entidad financiera 67431 3.366468
Teléfonos SAS 17534 0.875379
Teléfono web 12654 0.631746
Email 10779 0.538138
Otro 541 0.027009
Teléfonos Officers 441 0.022017
Correo postal sin cupón 227 0.011333
Teléfono Herencias y Legados 6 0.000300
SMS 2 0.000100
Redes Sociales 1 0.000050
msf_leadsource2__c: Canal de modificación agrupación 2.
Hay un 16% de vacios. La distribucion se concentra en telemarketing.
Analsis de distribución por variables
-> msf_leadsource3__c: Variable string
In [106]:
# Vamos a realizar analisis por cada variable
var = "msf_leadsource3__c"
In [107]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_leadsource3__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_leadsource3__c es 324964. Lo que supone un 16.22371030928813%
In [108]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[108]:
# Tot % Tot
Teléfono 942686 47.063258
Personal 445916 22.262195
324964 16.223710
Online 135185 6.749062
Correo postal 86296 4.308297
Entidad financiera 67431 3.366468
Otro 541 0.027009
msf_leadsource3__c: Canal de modificación agrpacion 3.
Hay un 16% de vacios. La distribucion se concentra en telemarketing. Esta variable más agrupada puede ser mejor para el analisis que las anteriores.
In [109]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_mod_cuota.append("msf_leadsource1__c")
col_to_delete_mod_cuota.append("msf_leadsource2__c")
col_to_delete_mod_cuota
Out[109]:
['name', 'msf_changeamount__c', 'msf_leadsource1__c', 'msf_leadsource2__c']
Analsis de distribución por variables
-> msf_newamount__c: Variable numerica
In [110]:
# Vamos a realizar analisis por cada variable
var = "msf_newamount__c"
In [111]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_newamount__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_newamount__c es 0. Lo que supone un 0.0%
In [112]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[112]:
# Tot % Tot
1.000000e+01 397732 19.856626
1.500000e+01 214087 10.688216
2.000000e+01 156745 7.825438
5.000000e+00 152635 7.620247
1.200000e+01 108041 5.393908
... ... ...
3.030000e+02 1 0.000050
1.809000e+01 1 0.000050
4.360000e+02 1 0.000050
6.851500e+02 1 0.000050
6.507972e+08 1 0.000050

1284 rows × 2 columns

msf_newamount__c: Nueva cuota.
No hay vacios. Muy distribuida.
Analsis de distribución por variables
-> msf_newannualizedquota__c: Variable numerica
In [113]:
# Vamos a realizar analisis por cada variable
var = "msf_newannualizedquota__c"
In [114]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_newannualizedquota__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_newannualizedquota__c es 0. Lo que supone un 0.0%
In [115]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[115]:
# Tot % Tot
1.200000e+02 411248 20.531408
1.800000e+02 201969 10.083229
6.000000e+01 183545 9.163418
2.400000e+02 139200 6.949510
1.440000e+02 102569 5.120720
... ... ...
1.262040e+03 1 0.000050
6.970000e+01 1 0.000050
6.851500e+02 1 0.000050
2.404048e+05 1 0.000050
7.809566e+09 1 0.000050

1681 rows × 2 columns

msf_newannualizedquota__c: Nueva cuota anualizada.
No hay vacios. Muy distribuida.
Analsis de distribución por variables
-> msf_newrecurringperiod__c: Variable string
In [116]:
# Vamos a realizar analisis por cada variable
var = "msf_newrecurringperiod__c"
In [117]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_newrecurringperiod__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_newrecurringperiod__c es 235. Lo que supone un 0.01173229010808185%
In [118]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[118]:
# Tot % Tot
Monthly 1536735 76.720940
Yearly 251355 12.548808
Quarterly 170741 8.524183
Semestral 34468 1.720802
Bimensual 9485 0.473535
235 0.011732
msf_newrecurringperiod__c: msf_newrecurringperiod__c.
Exite un 0.01% de vacios. La mayoría se concentra en mensual.
Analsis de distribución por variables
-> msf_changedate__c: Variable fecha
In [119]:
# Vamos a realizar analisis por cada variable
var = "msf_changedate__c"
In [120]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_changedate__c es 186. Lo que supone un 0.00928598280894989%
El nº de vacios para la variable msf_changedate__c es 0. Lo que supone un 0.0%
In [121]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[121]:
# Tot % Tot
2018-01-03 15448 0.771307
2017-02-02 13291 0.663610
2017-12-04 12659 0.632055
2018-02-01 11043 0.551369
2014-12-02 10917 0.545078
... ... ...
1990-01-10 1 0.000050
2000-12-07 1 0.000050
1991-09-03 1 0.000050
1996-11-09 1 0.000050
1991-11-18 1 0.000050

7992 rows × 2 columns

msf_changedate__c: Fecha de modificacion de la cuota.
Hay 186 vacios. Se puede utilizar para saber desde hace cuanto no se modifica la cuota, y priorizar a la hpra de seleccionar registros para el modelo y que sea muestra reciente.

3. Tabla contactos¶

In [122]:
# Vamos a analizar la tabla contactos
df=df_contactos
In [123]:
# Se crea una lista por ahora vacia, en la que se irán añadiendo las variables que se van a eliminar del dataset por motivos varios: no utilidad, gran volumen de nulos, ...
col_to_delete_contactos=list()
Analsis de distribución por variables
-> msf_seniority__c: Variable numerica
In [124]:
# Vamos a realizar analisis por cada variable
var = "msf_seniority__c"
In [125]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_seniority__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_seniority__c es 0. Lo que supone un 0.0%
In [126]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[126]:
# Tot % Tot
0.0 534123 29.617244
1.0 87701 4.863041
6.0 84533 4.687374
8.0 76878 4.262903
7.0 74742 4.144461
9.0 73643 4.083521
13.0 61510 3.410744
2.0 55231 3.062572
5.0 53150 2.947180
12.0 52314 2.900823
10.0 50959 2.825688
4.0 49842 2.763750
29.0 48213 2.673422
18.0 44670 2.476962
3.0 42963 2.382308
11.0 41647 2.309336
14.0 41012 2.274125
19.0 40508 2.246178
17.0 36854 2.043563
15.0 33506 1.857915
16.0 32733 1.815052
20.0 27078 1.501481
23.0 26493 1.469043
22.0 21703 1.203436
25.0 19070 1.057436
24.0 17734 0.983354
21.0 13985 0.775471
28.0 13981 0.775250
27.0 13960 0.774085
31.0 13612 0.754789
26.0 8493 0.470939
30.0 7239 0.401404
32.0 1634 0.090606
34.0 552 0.030609
35.0 550 0.030498
33.0 370 0.020517
36.0 201 0.011145
37.0 32 0.001774
msf_seniority__c: Número de años desde la fecha de su primera aportación económica hasta día de hoy.
Se puede observar que está bastante distribuido, siendo en 0 donde se acumula la mayor parte de la población. Se analizará posteriormente si la categorización de la variable en grupos pueda dar buenos resultados.
Analsis de distribución por variables
-> npo02__best_gift_year__c: Variable numerica
In [127]:
# Vamos a realizar analisis por cada variable
var = "npo02__best_gift_year__c"
In [128]:
# Analizamos nulos
count_nulos(df_contactos,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__best_gift_year__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__best_gift_year__c es 709207. Lo que supone un 39.32569192184401%
Out[128]:
['npo02__best_gift_year__c']
In [129]:
# Analizamos posibles valores de la variable
freq_variables(df_contactos,var)
Out[129]:
# Tot % Tot
709207 39.325692
2018 303667 16.838405
2022 185032 10.260067
2021 93074 5.160975
2020 90828 5.036434
2019 77054 4.272662
2023 55899 3.099612
2010 29210 1.619701
1994 28224 1.565027
2017 21245 1.178040
2005 15932 0.883433
2014 14681 0.814065
2011 14643 0.811958
2004 13160 0.729725
2000 12659 0.701944
2015 11996 0.665181
2001 11403 0.632299
1998 11363 0.630081
2013 10940 0.606626
2016 9948 0.551619
2003 9537 0.528829
2008 8465 0.469386
1999 8142 0.451476
2009 7599 0.421366
1996 6869 0.380888
2012 6795 0.376784
2006 6723 0.372792
1992 6238 0.345899
2007 5562 0.308414
2002 4753 0.263555
1997 4491 0.249027
1995 4064 0.225350
1993 2470 0.136962
1991 624 0.034601
1989 435 0.024121
1990 212 0.011755
1988 187 0.010369
1987 88 0.004880
npo02__best_gift_year__c: Año fiscal en que se ha realizado mayor importe total.
Se puede observar que hay casi un 40% de los registros a vacio. Se analizará posteriormente por si son clientes sin ninguna aportacion y poder analizar si es vacio con sentido o no.
Analsis de distribución por variables
-> msf_birthyear__c: Variable numerica
In [130]:
# Vamos a realizar analisis por cada variable
var = "msf_birthyear__c"
In [131]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_birthyear__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_birthyear__c es 978577. Lo que supone un 54.26232062543425%
Out[131]:
['npo02__best_gift_year__c', 'msf_birthyear__c']
In [132]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[132]:
# Tot % Tot
978577 54.262321
1964 18538 1.027936
1963 18278 1.013519
1965 18234 1.011080
1968 18067 1.001819
1959 17962 0.995997
1958 17951 0.995387
1962 17871 0.990951
1966 17855 0.990064
1973 17816 0.987901
1974 17776 0.985683
1975 17754 0.984463
1957 17705 0.981746
1961 17701 0.981525
1960 17684 0.980582
1972 17602 0.976035
1967 17569 0.974205
1976 17476 0.969048
1971 17399 0.964779
1969 17397 0.964668
1970 17285 0.958457
1977 16927 0.938606
1978 16689 0.925409
1956 16033 0.889034
1979 16023 0.888479
1980 15320 0.849498
1955 14913 0.826929
1981 14485 0.803197
1954 13777 0.763938
1982 13359 0.740760
1953 13105 0.726675
1983 12665 0.702277
1952 12655 0.701723
1984 11754 0.651762
1951 11508 0.638121
1950 11123 0.616773
1985 10843 0.601247
1949 10698 0.593207
1948 10334 0.573023
1986 9804 0.543634
1987 9147 0.507203
1947 9075 0.503211
1988 8543 0.473711
1989 8272 0.458684
1946 8261 0.458074
1945 8234 0.456577
1991 7771 0.430904
1990 7748 0.429628
1992 7676 0.425636
1993 7401 0.410387
1994 7375 0.408945
1996 7255 0.402291
1995 7246 0.401792
1944 7141 0.395970
1943 7129 0.395305
1997 7041 0.390425
1999 6720 0.372626
1998 6627 0.367469
2000 6424 0.356212
2001 5559 0.308248
1942 5491 0.304477
1940 5175 0.286955
1941 4961 0.275089
2002 4619 0.256125
2003 3607 0.200009
1936 3586 0.198845
1938 3348 0.185647
1937 3315 0.183818
1939 3294 0.182653
1935 3234 0.179326
1934 2862 0.158699
1933 2513 0.139346
1932 2359 0.130807
1930 2069 0.114727
2004 2032 0.112675
1931 2025 0.112287
1929 1485 0.082344
1928 1386 0.076854
1927 1123 0.062271
1926 959 0.053177
1925 860 0.047687
1924 743 0.041200
1923 589 0.032660
1922 545 0.030220
1921 419 0.023234
1920 338 0.018742
2020 313 0.017356
1919 295 0.016358
2005 238 0.013197
1918 194 0.010757
2019 181 0.010036
2006 163 0.009038
1917 158 0.008761
1916 132 0.007319
2017 131 0.007264
2008 123 0.006820
2007 116 0.006432
2016 114 0.006321
1915 103 0.005711
2014 93 0.005157
2021 85 0.004713
2015 85 0.004713
2013 85 0.004713
2018 77 0.004270
1914 75 0.004159
2012 74 0.004103
2010 71 0.003937
2009 68 0.003771
2011 58 0.003216
1913 57 0.003161
1911 41 0.002273
1912 33 0.001830
2022 23 0.001275
1909 22 0.001220
1910 21 0.001164
2023 19 0.001054
1906 12 0.000665
1904 12 0.000665
1908 11 0.000610
1907 9 0.000499
1905 8 0.000444
1903 6 0.000333
1900 6 0.000333
1902 5 0.000277
1901 3 0.000166
1897 2 0.000111
1800 1 0.000055
1893 1 0.000055
1712 1 0.000055
msf_birthyear__c: .
Se puede observar que hay más de un 54% de los registros a vacio.
Analsis de distribución por variables
-> msf_entrycampaign__c: Variable string
In [133]:
# Vamos a realizar analisis por cada variable
var = "msf_entrycampaign__c"
In [134]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_entrycampaign__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_entrycampaign__c es 8854. Lo que supone un 0.49095634458769705%
In [135]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[135]:
# Tot % Tot
7013Y000001mrCzQAI 184255 10.216982
7013Y000001vYkXQAU 60467 3.352909
7013Y000001mrC7QAI 60346 3.346200
7013Y000001mr1MQAQ 45720 2.535185
7013Y000001mr4CQAQ 39785 2.206087
... ... ...
7013Y000001vQQaQAM 1 0.000055
7013Y000001mrPEQAY 1 0.000055
7013Y000001va2hQAA 1 0.000055
7013Y000001mrHCQAY 1 0.000055
7013Y000001mre3QAA 1 0.000055

5356 rows × 2 columns

msf_entrycampaign__c: .
Se puede observar que practicamente no hay vacios. Con la incorporación de información sobre el tipo de campaña puede ser util en el modelo.
Analsis de distribución por variables
-> LeadSource: Variable categorica
In [136]:
# Vamos a realizar analisis por cada variable
var = "leadsource"
In [137]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable leadsource es 0. Lo que supone un 0.0%
El nº de vacios para la variable leadsource es 8860. Lo que supone un 0.491289045973232%
In [138]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[138]:
# Tot % Tot
Otro 518507 28.751333
Telemarketing 413225 22.913422
Persona a persona 354960 19.682614
Web MSF 137215 7.608603
Web terceros 96714 5.362814
Cupón 81431 4.515368
Personal con tablet 73165 4.057016
Teléfono campaña 39125 2.169490
Web campaña 28490 1.579777
Eventos 13171 0.730335
8860 0.491289
Redes Sociales 7426 0.411773
Entidad financiera 6672 0.369964
Email a Bodas 5654 0.313516
Teléfono web 5216 0.289228
Plataforma iniciativas 4622 0.256291
Teléfono SAS 2520 0.139735
Email a Empresas 2286 0.126759
Email a SAS 2206 0.122323
Email a Iniciativas Solidarias 591 0.032771
Correo postal sin cupón 475 0.026339
Email a One to one 364 0.020184
Teléfono Officers 185 0.010258
Email herencias 143 0.007929
Teléfono Herencias y Legados 98 0.005434
TelEfono officers 56 0.003105
SMS 16 0.000887
Email a officers Mid Donors 10 0.000555
Cloud page 10 0.000555
Email Presidente/a MSF 2 0.000111
Email Director/a General 2 0.000111
Tel?fono SAS 2 0.000111
leadsource: Canal principal.
Se puede observar que casi no hay vacios. La mayor parte es Persona a Persona.
Analsis de distribución por variables
-> msf_firstcampaigncolaborationchannel__c: Variable categorica
In [139]:
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaigncolaborationchannel__c"
In [140]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstcampaigncolaborationchannel__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_firstcampaigncolaborationchannel__c es 643136. Lo que supone un 35.66203971456439%
Out[140]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c']
In [141]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[141]:
# Tot % Tot
643136 35.662040
Persona a persona 311433 17.269032
Otro 287825 15.959963
Telemarketing 171639 9.517422
Web MSF 104919 5.817783
Cupón 76517 4.242885
Personal con tablet 68111 3.776771
Web terceros 65274 3.619458
Teléfono campaña 37964 2.105113
Web campaña 11214 0.621819
Teléfono web 5200 0.288341
Entidad financiera 4822 0.267381
Plataforma iniciativas 4774 0.264719
Teléfono SAS 2876 0.159475
Email a SAS 1945 0.107851
Eventos 1470 0.081512
web campaña 1119 0.062049
Email a Bodas 888 0.049240
Email a Empresas 877 0.048630
Correo postal sin cupón 629 0.034878
cupón 258 0.014306
Teléfono Officers 195 0.010813
Web MSF Mi perfil 189 0.010480
Email herencias 59 0.003272
Teléfono Herencias y Legados 30 0.001664
Email a Iniciativas Solidarias 22 0.001220
Email a One to one 17 0.000943
sms 7 0.000388
Email a officers Mid Donors 4 0.000222
Cloud page 3 0.000166
Email a one to one 2 0.000111
Email Director/a General 1 0.000055
msf_firstcampaigncolaborationchannel__c: Canal por el que realizó la primera donación.
Se puede observar que hay un 35% de vacios.
In [142]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("msf_firstcampaigncolaborationchannel__c")
col_to_delete_contactos
Out[142]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c']
Analsis de distribución por variables
-> npo02__AverageAmount__c: Variable numerica
In [143]:
# Vamos a realizar analisis por cada variable
var = "npo02__averageamount__c"
In [144]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__averageamount__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__averageamount__c es 0. Lo que supone un 0.0%
In [145]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[145]:
# Tot % Tot
0.0 1803419 100.0
npo02__averageamount__c: Media del total de aportaciones.
Se puede observar que no hay vacios pero está informado a 0 para todos los casos.
In [146]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("npo02__averageamount__c")
col_to_delete_contactos
Out[146]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c']
Analsis de distribución por variables
-msf_isactivedonor__c: Variable categorica
In [147]:
# Vamos a realizar analisis por cada variable
var = "msf_isactivedonor__c"
In [148]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_isactivedonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_isactivedonor__c es 26966. Lo que supone un 1.4952709270557756%
In [149]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[149]:
# Tot % Tot
Nunca 1150813 63.812847
Exdonante 512353 28.410092
Donante 113287 6.281790
26966 1.495271
msf_isactivedonor__c: donante activo
Se puede observar como la mayor parte nunca han realizado donaciones puntuales. Se puede plantear un booleano en el dataset inicial marcando como 1 a aquellos que si hayan realizado donaciones puntuales además de las periodicas y 0 en caso contrario.
Analsis de distribución por variables
-> msf_isactiverecurringdonor__c: Variable categorica
In [150]:
# Vamos a realizar analisis por cada variable
var = "msf_isactiverecurringdonor__c"
In [151]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_isactiverecurringdonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_isactiverecurringdonor__c es 26966. Lo que supone un 1.4952709270557756%
In [152]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[152]:
# Tot % Tot
Nunca 783149 43.425793
Baja 511080 28.339504
Socio 482224 26.739432
26966 1.495271
msf_isactiverecurringdonor__c: indicador de socio recurrente.
Se puede observar que no hay vacios, se utilizará para filtrar los socios a los que introducir en el modelo.
In [153]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("msf_isactiverecurringdonor__c")
col_to_delete_contactos
Out[153]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c']
Analsis de distribución por variables
-> npsp__deceased__c: Variable categorica
In [154]:
# Vamos a realizar analisis por cada variable
var = "npsp__deceased__c"
In [155]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__deceased__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npsp__deceased__c es 0. Lo que supone un 0.0%
In [156]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[156]:
# Tot % Tot
False 1778101 98.596111
True 25318 1.403889
npsp__deceased__c: Indicador de fallecido
Se puede observar que no hay vacios, solo el 2% han fallecido. Esto quiere decir que se podrán usar en el modelo para prdecir, pero no para aplicar.
Analsis de distribución por variables
-> msf_begindatemsf__c: Variable categorica
In [157]:
# Vamos a realizar analisis por cada variable
var = "msf_begindatemsf__c"
In [158]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_begindatemsf__c es 1. Lo que supone un 5.545023092248668e-05%
El nº de vacios para la variable msf_begindatemsf__c es 0. Lo que supone un 0.0%
In [159]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[159]:
# Tot % Tot
2004-11-10 33850 1.876991
2013-03-28 19100 1.059100
2015-12-22 14283 0.791996
2022-01-14 13308 0.737932
2010-03-29 11776 0.652982
... ... ...
1990-08-31 1 0.000055
1991-02-21 1 0.000055
1990-08-15 1 0.000055
1990-07-19 1 0.000055
1994-09-18 1 0.000055

11146 rows × 2 columns

msf_begindatemsf__c: Fecha de entrada en MSF.
Se puede observar que no hay vacios, se podrá tranformar en "tiempo en MSF" teniendo en cuenta la fecha de las tablas.
Analsis de distribución por variables
-> msf_fechacambiolevelrelacion__c: Variable categorica
In [160]:
# Vamos a realizar analisis por cada variable
var = "msf_fechacambiolevelrelacion__c"
In [161]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_fechacambiolevelrelacion__c es 2204. Lo que supone un 0.12221230895316064%
El nº de vacios para la variable msf_fechacambiolevelrelacion__c es 0. Lo que supone un 0.0%
In [162]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[162]:
# Tot % Tot
2020-03-28 1471390 81.688749
2022-02-10 19708 1.094150
2020-07-20 19554 1.085601
2022-01-15 13175 0.731451
2021-07-30 9205 0.511044
2022-05-11 7309 0.405782
2022-03-22 7297 0.405115
2020-09-19 7118 0.395178
2021-04-15 7054 0.391625
2020-09-22 6608 0.366863
2022-06-04 5376 0.298465
2023-01-26 4782 0.265487
2022-06-17 4682 0.259936
2022-05-06 4607 0.255772
2022-12-03 4578 0.254162
2023-02-21 4232 0.234953
2022-10-21 4112 0.228290
2022-01-02 3213 0.178380
2022-09-28 2308 0.128136
2020-09-20 2281 0.126637
2022-12-20 2235 0.124083
2023-01-03 1940 0.107705
2020-09-21 1746 0.096935
2022-11-17 1495 0.083000
2022-06-14 1455 0.080779
2021-01-04 1360 0.075505
2022-07-22 1227 0.068121
2023-01-02 1192 0.066178
2023-02-10 1026 0.056962
2022-03-23 1018 0.056517
2022-03-05 1005 0.055796
2023-02-09 985 0.054685
2021-05-20 974 0.054075
2023-02-23 898 0.049855
2022-09-08 879 0.048800
2021-01-20 821 0.045580
2023-02-15 804 0.044637
2023-03-28 797 0.044248
2022-12-28 784 0.043526
2022-03-12 754 0.041861
2022-01-19 747 0.041472
2022-10-27 708 0.039307
2023-02-11 707 0.039251
2023-02-08 696 0.038641
2023-04-05 682 0.037863
2022-05-10 681 0.037808
2022-05-19 662 0.036753
2022-03-11 660 0.036642
2021-10-07 654 0.036309
2022-03-09 649 0.036031
2022-12-23 646 0.035865
2020-12-04 642 0.035643
2022-03-07 631 0.035032
2022-03-10 623 0.034588
2022-03-15 594 0.032978
2022-03-04 593 0.032922
2021-06-18 593 0.032922
2023-05-13 588 0.032645
2023-04-19 578 0.032089
2022-09-23 577 0.032034
2021-11-18 568 0.031534
2022-10-06 566 0.031423
2021-03-04 558 0.030979
2021-07-28 556 0.030868
2022-03-17 536 0.029758
2023-02-12 533 0.029591
2022-03-08 533 0.029591
2022-03-24 528 0.029314
2020-09-23 520 0.028869
2022-07-07 519 0.028814
2023-02-14 509 0.028259
2022-03-16 508 0.028203
2023-03-16 506 0.028092
2023-02-18 501 0.027815
2021-01-01 496 0.027537
2021-07-08 489 0.027148
2023-02-17 488 0.027093
2022-03-25 488 0.027093
2022-02-24 487 0.027037
2021-06-09 482 0.026760
2021-02-05 480 0.026649
2022-06-08 477 0.026482
2022-02-05 476 0.026427
2020-10-03 475 0.026371
2021-09-07 475 0.026371
2023-02-16 474 0.026316
2022-11-24 470 0.026093
2021-05-13 470 0.026093
2021-06-25 468 0.025982
2023-03-30 465 0.025816
2021-01-06 463 0.025705
2022-04-20 462 0.025649
2021-08-07 461 0.025594
2023-04-26 460 0.025538
2022-05-24 454 0.025205
2021-01-28 454 0.025205
2021-03-11 440 0.024428
2021-01-03 439 0.024372
2022-02-15 435 0.024150
2022-03-18 434 0.024095
2020-11-20 427 0.023706
2020-11-27 426 0.023651
2023-02-07 424 0.023540
2022-01-06 416 0.023096
2020-11-06 413 0.022929
2022-07-01 410 0.022762
2021-05-08 403 0.022374
2023-04-21 400 0.022207
2022-02-03 392 0.021763
2021-06-23 390 0.021652
2021-12-03 388 0.021541
2020-09-25 381 0.021152
2022-09-30 379 0.021041
2023-05-04 376 0.020875
2021-03-18 375 0.020819
2023-07-05 375 0.020819
2023-03-03 374 0.020764
2023-03-01 373 0.020708
2023-02-24 373 0.020708
2021-11-13 373 0.020708
2021-12-22 369 0.020486
2021-04-14 369 0.020486
2021-04-17 367 0.020375
2021-06-03 365 0.020264
2020-12-25 363 0.020153
2023-01-18 363 0.020153
2022-11-30 361 0.020042
2023-01-04 359 0.019931
2021-05-27 357 0.019820
2021-03-06 356 0.019764
2023-04-14 356 0.019764
2021-04-22 355 0.019709
2022-06-23 352 0.019542
2021-01-21 349 0.019376
2021-06-05 349 0.019376
2021-04-29 349 0.019376
2023-03-24 348 0.019320
2022-05-28 348 0.019320
2022-08-06 347 0.019265
2022-11-18 345 0.019154
2020-12-19 343 0.019043
2023-04-20 341 0.018932
2021-11-07 341 0.018932
2021-02-11 338 0.018765
2023-03-10 338 0.018765
2022-09-04 338 0.018765
2020-12-24 338 0.018765
2020-10-24 337 0.018710
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2021-04-04 28 0.001555
2022-01-24 28 0.001555
2021-08-09 27 0.001499
2021-02-08 27 0.001499
2020-11-01 27 0.001499
2021-03-29 27 0.001499
2020-11-16 27 0.001499
2021-08-16 27 0.001499
2023-02-26 26 0.001443
2020-11-09 26 0.001443
2022-11-20 26 0.001443
2022-09-05 26 0.001443
2021-12-13 25 0.001388
2021-01-30 25 0.001388
2022-04-10 25 0.001388
2022-04-24 25 0.001388
2022-01-17 25 0.001388
2022-12-05 25 0.001388
2021-01-10 24 0.001332
2022-01-23 24 0.001332
2021-01-11 23 0.001277
2021-02-14 23 0.001277
2021-11-02 23 0.001277
2021-10-18 23 0.001277
2020-11-15 23 0.001277
2021-08-23 23 0.001277
2021-02-15 22 0.001221
2021-09-12 22 0.001221
2021-04-26 22 0.001221
2020-11-29 21 0.001166
2022-01-31 21 0.001166
2021-04-19 21 0.001166
2021-02-22 21 0.001166
2022-02-13 21 0.001166
2021-06-06 21 0.001166
2021-08-22 20 0.001110
2021-05-10 20 0.001110
2023-03-27 20 0.001110
2022-10-31 20 0.001110
2022-02-27 19 0.001055
2021-08-08 19 0.001055
2022-06-06 19 0.001055
2022-11-27 19 0.001055
2022-02-04 19 0.001055
2021-05-07 19 0.001055
2022-11-21 19 0.001055
2021-07-04 19 0.001055
2021-11-22 18 0.000999
2021-11-29 18 0.000999
2021-04-25 18 0.000999
2021-04-18 18 0.000999
2021-06-27 18 0.000999
2021-05-03 18 0.000999
2022-05-22 18 0.000999
2021-01-17 18 0.000999
2020-10-13 18 0.000999
2021-10-17 17 0.000944
2023-05-28 17 0.000944
2021-06-07 17 0.000944
2021-08-05 17 0.000944
2022-04-18 17 0.000944
2022-05-05 17 0.000944
2023-03-13 17 0.000944
2022-05-01 17 0.000944
2022-05-29 17 0.000944
2021-11-15 17 0.000944
2022-04-17 17 0.000944
2021-03-21 16 0.000888
2020-09-28 16 0.000888
2022-06-19 16 0.000888
2021-08-30 16 0.000888
2021-06-20 16 0.000888
2021-05-24 16 0.000888
2021-12-09 16 0.000888
2021-09-06 16 0.000888
2021-05-17 16 0.000888
2021-11-14 15 0.000833
2021-06-14 15 0.000833
2023-01-15 15 0.000833
2023-01-30 15 0.000833
2020-04-11 15 0.000833
2023-04-03 15 0.000833
2021-02-21 15 0.000833
2022-02-20 15 0.000833
2022-12-11 15 0.000833
2021-04-05 15 0.000833
2021-09-26 15 0.000833
2022-05-15 15 0.000833
2021-04-12 15 0.000833
2021-03-07 15 0.000833
2022-08-16 15 0.000833
2022-10-30 15 0.000833
2022-05-16 15 0.000833
2023-01-23 14 0.000777
2021-08-29 14 0.000777
2020-10-18 14 0.000777
2023-06-19 14 0.000777
2022-05-30 14 0.000777
2023-06-12 14 0.000777
2022-07-17 14 0.000777
2022-06-13 14 0.000777
2021-05-30 14 0.000777
2021-02-28 14 0.000777
2021-08-15 14 0.000777
2021-01-18 13 0.000722
2022-10-24 13 0.000722
2023-05-02 13 0.000722
2021-11-28 13 0.000722
2022-05-09 13 0.000722
2021-07-25 13 0.000722
2021-06-21 13 0.000722
2022-11-13 13 0.000722
2021-10-31 13 0.000722
2023-03-12 13 0.000722
2022-08-08 13 0.000722
2022-06-20 13 0.000722
2021-03-08 13 0.000722
2021-11-21 13 0.000722
2021-09-13 13 0.000722
2022-01-16 13 0.000722
2023-04-24 12 0.000666
2021-03-22 12 0.000666
2021-10-13 12 0.000666
2021-05-31 12 0.000666
2023-06-05 12 0.000666
2023-05-29 12 0.000666
2022-11-07 12 0.000666
2022-02-14 12 0.000666
2023-04-30 12 0.000666
2022-08-22 12 0.000666
2023-03-19 12 0.000666
2022-10-23 12 0.000666
2023-01-16 12 0.000666
2022-04-25 11 0.000611
2021-12-06 11 0.000611
2020-10-02 11 0.000611
2021-09-20 11 0.000611
2023-04-08 11 0.000611
2021-03-15 11 0.000611
2021-10-11 11 0.000611
2021-07-11 11 0.000611
2022-06-26 11 0.000611
2021-11-08 11 0.000611
2021-05-09 11 0.000611
2023-07-09 11 0.000611
2023-01-29 11 0.000611
2022-09-12 11 0.000611
2021-04-11 11 0.000611
2023-04-10 11 0.000611
2023-05-14 11 0.000611
2022-07-25 11 0.000611
2021-10-10 11 0.000611
2023-03-20 10 0.000555
2022-08-29 10 0.000555
2021-07-19 10 0.000555
2023-04-23 10 0.000555
2023-07-02 10 0.000555
2023-01-22 10 0.000555
2022-09-25 10 0.000555
2022-02-21 10 0.000555
2022-10-17 9 0.000500
2022-07-18 9 0.000500
2021-06-13 9 0.000500
2020-10-19 9 0.000500
2023-01-09 9 0.000500
2022-11-28 9 0.000500
2022-08-15 9 0.000500
2022-09-19 9 0.000500
2021-09-27 9 0.000500
2023-07-03 9 0.000500
2021-11-01 9 0.000500
2022-06-12 8 0.000444
2022-07-24 8 0.000444
2020-10-11 8 0.000444
2021-05-23 8 0.000444
2022-11-14 8 0.000444
2022-08-01 8 0.000444
2021-10-25 8 0.000444
2021-07-05 8 0.000444
2021-10-24 8 0.000444
2021-07-26 8 0.000444
2023-05-15 7 0.000389
2022-08-14 7 0.000389
2023-06-18 7 0.000389
2022-07-31 7 0.000389
2022-06-27 7 0.000389
2021-09-19 7 0.000389
2021-03-14 7 0.000389
2021-05-14 7 0.000389
2022-09-11 7 0.000389
2022-10-16 7 0.000389
2021-06-28 7 0.000389
2022-09-26 7 0.000389
2023-05-22 6 0.000333
2023-06-26 6 0.000333
2021-07-18 6 0.000333
2022-10-13 5 0.000278
2022-09-18 5 0.000278
2022-08-21 5 0.000278
2022-08-28 5 0.000278
2021-09-05 5 0.000278
2023-05-01 5 0.000278
2023-05-21 5 0.000278
2022-05-02 4 0.000222
2023-06-11 4 0.000222
2021-10-02 3 0.000167
2022-11-02 2 0.000111
2021-07-06 2 0.000111
2023-07-04 2 0.000111
2022-07-04 2 0.000111
2022-10-08 1 0.000056
2022-08-07 1 0.000056
2021-08-03 1 0.000056
2022-01-04 1 0.000056
2023-06-02 1 0.000056
msf_fechacambiolevelrelacion__c: Fecha de cambio de nivel de relación.
Se puede observar que aunque practicamente no hay vacios ni nulos, el 81% de la misma tiene fecha
Analsis de distribución por variables
-> msf_datefirstdonation__c: Variable fecha
In [163]:
# Vamos a realizar analisis por cada variable
var = "msf_datefirstdonation__c"
In [164]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datefirstdonation__c es 1195087. Lo que supone un 66.26785012246184%
El nº de vacios para la variable msf_datefirstdonation__c es 0. Lo que supone un 0.0%
Out[164]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c']
In [165]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[165]:
# Tot % Tot
2017-05-16 11149 1.832716
2010-02-01 5502 0.904440
2017-12-01 4984 0.819289
2020-07-01 4452 0.731837
2010-01-15 3777 0.620878
... ... ...
2007-01-28 1 0.000164
1991-03-19 1 0.000164
1990-02-13 1 0.000164
1992-01-28 1 0.000164
2013-04-14 1 0.000164

11049 rows × 2 columns

msf_datefirstdonation__c: Fecha de la primera donacion.
Se puede observar que hay más de un 66% de los registros a vacio.
Analsis de distribución por variables
-> msf_datefirstrecurringdonorquota__c: Variable fecha
In [166]:
# Vamos a realizar analisis por cada variable
var = "msf_datefirstrecurringdonorquota__c"
In [167]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datefirstrecurringdonorquota__c es 858069. Lo que supone un 47.58012419742722%
El nº de vacios para la variable msf_datefirstrecurringdonorquota__c es 0. Lo que supone un 0.0%
Out[167]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c']
In [168]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[168]:
# Tot % Tot
2006-01-05 9932 1.050616
2011-01-03 9921 1.049453
2009-01-02 9654 1.021209
2021-01-05 8946 0.946316
2014-11-03 8567 0.906225
2015-01-02 8523 0.901571
2005-02-04 8472 0.896176
2014-12-02 8309 0.878934
2005-01-04 8185 0.865817
2012-01-02 8034 0.849844
2016-07-01 7977 0.843814
2004-02-01 7780 0.822976
2004-01-01 7525 0.796001
2015-10-01 7445 0.787539
2007-01-04 6987 0.739091
2016-01-04 6778 0.716983
2017-04-03 6649 0.703337
2010-01-04 6590 0.697096
2003-01-01 6482 0.685672
2015-11-03 6357 0.672449
2013-01-02 6285 0.664833
2016-04-01 6213 0.657217
2015-12-02 6209 0.656794
2003-03-01 6125 0.647908
2017-06-01 6057 0.640715
2016-08-01 6024 0.637224
2015-03-02 5930 0.627281
2015-02-02 5913 0.625483
2014-05-05 5836 0.617337
2016-10-03 5733 0.606442
2016-12-01 5723 0.605384
2015-04-01 5646 0.597239
2015-05-04 5559 0.588036
2017-07-03 5550 0.587084
2016-05-02 5498 0.581584
2016-11-02 5485 0.580208
2017-12-04 5456 0.577141
2010-02-01 5447 0.576189
2016-06-01 5400 0.571217
2017-01-02 5374 0.568467
2015-06-02 5328 0.563601
2006-02-03 5327 0.563495
2016-03-01 5319 0.562649
2017-08-01 5305 0.561168
2017-03-02 5278 0.558312
2009-02-03 5192 0.549215
2016-02-01 5139 0.543608
2014-01-02 5138 0.543502
2015-08-03 5125 0.542127
2014-08-01 4955 0.524144
2017-05-02 4884 0.516634
2014-06-05 4880 0.516211
2015-07-01 4823 0.510181
2017-02-02 4793 0.507008
2018-01-03 4793 0.507008
2009-12-02 4788 0.506479
2018-02-01 4744 0.501825
2018-06-01 4697 0.496853
2010-12-02 4667 0.493680
2018-03-01 4629 0.489660
2014-10-02 4554 0.481726
2014-04-02 4549 0.481197
2000-02-01 4504 0.476437
2012-02-01 4426 0.468186
2007-02-05 4376 0.462897
2013-02-01 4356 0.460782
2017-09-01 4342 0.459301
2011-12-01 4336 0.458666
2014-07-02 4277 0.452425
2018-07-02 4214 0.445761
2017-11-02 4202 0.444491
2014-02-03 4167 0.440789
2022-04-02 4157 0.439731
2018-08-01 4146 0.438568
2018-12-03 4109 0.434654
2011-02-01 4044 0.427778
2017-10-02 4002 0.423335
2018-04-03 3975 0.420479
2018-11-02 3934 0.416142
2008-12-01 3868 0.409161
2013-12-02 3847 0.406939
2005-03-04 3824 0.404506
2013-11-04 3805 0.402496
2019-12-02 3789 0.400804
2008-01-03 3781 0.399958
2020-02-03 3769 0.398688
2000-01-01 3719 0.393399
2014-03-03 3718 0.393293
2019-01-02 3698 0.391178
1994-10-01 3689 0.390226
2008-02-04 3668 0.388004
2019-11-04 3644 0.385466
2016-09-01 3614 0.382292
2011-04-01 3614 0.382292
2006-12-02 3592 0.379965
2020-03-02 3576 0.378273
2019-06-03 3564 0.377003
2021-04-02 3550 0.375522
2021-03-02 3533 0.373724
2022-12-02 3531 0.373512
2020-01-02 3525 0.372878
2018-05-03 3520 0.372349
2013-05-02 3516 0.371926
2014-09-03 3514 0.371714
2021-07-02 3491 0.369281
2019-08-01 3489 0.369070
2013-08-02 3472 0.367271
2019-05-02 3458 0.365790
2019-04-01 3450 0.364944
2021-06-02 3422 0.361982
2019-07-01 3411 0.360819
2023-03-02 3388 0.358386
2022-07-05 3378 0.357328
2019-02-01 3371 0.356588
2013-06-03 3351 0.354472
2001-03-01 3338 0.353097
2011-03-01 3329 0.352145
1995-02-01 3309 0.350029
2023-04-04 3299 0.348971
2011-08-02 3284 0.347385
2018-10-02 3267 0.345586
2012-12-03 3224 0.341038
2023-06-02 3214 0.339980
2013-03-01 3173 0.335643
2019-03-01 3145 0.332681
2007-12-02 3138 0.331941
2013-04-02 3135 0.331623
2022-11-03 3133 0.331412
2019-10-02 3129 0.330989
2012-11-02 3101 0.328027
2015-09-01 3095 0.327392
2023-07-04 3090 0.326863
2022-06-02 3090 0.326863
2021-10-02 3081 0.325911
2013-07-01 3057 0.323372
2001-02-01 3052 0.322843
2021-12-02 3037 0.321257
2010-08-02 3034 0.320939
2021-05-04 3031 0.320622
2023-02-02 3003 0.317660
2004-03-01 2977 0.314910
2005-12-03 2966 0.313746
2022-10-04 2932 0.310150
2012-08-01 2918 0.308669
2021-11-03 2889 0.305601
2022-01-04 2796 0.295763
2022-08-02 2788 0.294917
1998-03-01 2785 0.294600
2010-03-01 2774 0.293436
2009-03-03 2766 0.292590
2011-11-02 2764 0.292378
2013-10-02 2744 0.290263
2018-09-03 2741 0.289946
2021-08-03 2733 0.289099
2023-01-03 2726 0.288359
2012-03-01 2704 0.286032
1999-01-01 2671 0.282541
2021-02-02 2665 0.281906
2022-03-02 2663 0.281695
1994-02-01 2653 0.280637
2012-06-04 2649 0.280214
2022-05-03 2647 0.280002
2012-04-02 2627 0.277886
2011-05-02 2597 0.274713
2013-09-02 2590 0.273973
2022-02-02 2538 0.268472
2008-08-08 2535 0.268155
2020-04-02 2480 0.262337
2002-01-01 2476 0.261914
2010-04-01 2454 0.259586
2012-07-02 2451 0.259269
2011-07-01 2432 0.257259
2006-11-03 2415 0.255461
2011-06-01 2406 0.254509
2010-07-01 2364 0.250066
2008-04-04 2363 0.249960
2023-05-03 2331 0.246575
2020-05-03 2326 0.246046
2007-04-02 2315 0.244883
2007-03-02 2312 0.244566
2011-09-02 2278 0.240969
2011-10-04 2247 0.237690
2002-12-01 2244 0.237372
1999-02-01 2212 0.233987
2012-05-03 2203 0.233035
2008-03-03 2103 0.222457
2006-03-03 2090 0.221082
2009-04-02 2086 0.220659
2009-07-02 2080 0.220024
2012-10-01 2029 0.214630
2008-06-02 2025 0.214206
2010-06-02 2020 0.213677
2004-12-05 2000 0.211562
1996-02-01 1978 0.209235
2006-04-03 1971 0.208494
2007-10-04 1948 0.206061
2019-09-02 1913 0.202359
2001-01-01 1885 0.199397
2007-05-04 1875 0.198339
2007-09-03 1874 0.198233
2010-10-04 1874 0.198233
2007-07-04 1851 0.195800
2020-08-03 1830 0.193579
2005-11-03 1805 0.190935
1994-07-01 1800 0.190406
2003-12-01 1795 0.189877
2010-05-03 1767 0.186915
2005-08-02 1758 0.185963
2010-11-02 1716 0.181520
2009-08-03 1714 0.181309
2009-06-04 1707 0.180568
2009-10-02 1699 0.179722
2008-07-04 1689 0.178664
2009-05-04 1670 0.176654
2005-06-03 1660 0.175596
2006-06-02 1649 0.174433
1997-02-01 1647 0.174221
2008-05-02 1629 0.172317
2020-06-02 1626 0.172000
2005-07-04 1625 0.171894
2007-08-02 1584 0.167557
1998-02-01 1546 0.163537
2006-07-03 1528 0.161633
2009-09-02 1514 0.160152
2008-09-01 1513 0.160047
2009-11-02 1506 0.159306
1995-04-01 1480 0.156556
2000-03-01 1436 0.151901
2007-11-02 1434 0.151690
1994-01-01 1422 0.150420
2012-09-03 1422 0.150420
2022-09-02 1403 0.148411
2021-09-02 1394 0.147459
2020-07-01 1384 0.146401
2005-04-04 1365 0.144391
1992-11-01 1352 0.143016
1998-01-01 1348 0.142593
1994-09-01 1327 0.140371
2008-10-02 1293 0.136775
2008-11-03 1292 0.136669
2010-09-02 1286 0.136034
2005-05-04 1242 0.131380
2007-06-05 1226 0.129687
2020-09-01 1223 0.129370
2005-09-02 1180 0.124821
1995-03-01 1169 0.123658
2006-08-02 1121 0.118580
1999-06-01 1076 0.113820
2005-10-03 1055 0.111599
2006-05-04 973 0.102925
1999-03-01 960 0.101550
1997-01-01 930 0.098376
2002-04-01 880 0.093087
2002-02-01 878 0.092876
2004-04-01 875 0.092558
1994-03-01 866 0.091606
2006-09-04 862 0.091183
2003-11-01 850 0.089914
1995-01-01 839 0.088750
2003-06-01 830 0.087798
2006-10-02 826 0.087375
1995-07-01 821 0.086846
2003-04-01 795 0.084096
2002-05-01 780 0.082509
2004-11-04 769 0.081346
2001-04-01 756 0.079970
2000-05-01 744 0.078701
1999-07-01 710 0.075104
1994-04-01 691 0.073095
2003-08-01 683 0.072248
2004-05-01 667 0.070556
2004-06-01 662 0.070027
1992-12-01 662 0.070027
1995-10-01 661 0.069921
1996-04-01 651 0.068863
1999-05-01 643 0.068017
2000-04-01 634 0.067065
2001-08-01 617 0.065267
2004-08-01 609 0.064421
1999-12-01 591 0.062517
2020-12-02 589 0.062305
1998-04-01 577 0.061036
1996-12-01 573 0.060612
2003-05-01 555 0.058708
1998-09-01 549 0.058074
1998-12-01 531 0.056170
1994-06-01 524 0.055429
2000-01-13 516 0.054583
2001-12-01 513 0.054266
1996-01-01 510 0.053948
2001-07-01 505 0.053419
2004-10-06 499 0.052785
1998-11-01 492 0.052044
2004-07-01 490 0.051833
1993-11-01 489 0.051727
2002-11-01 485 0.051304
1996-03-01 477 0.050458
1996-06-01 476 0.050352
1995-06-01 466 0.049294
2002-08-01 463 0.048977
2003-10-01 455 0.048130
1998-05-01 447 0.047284
1992-06-01 445 0.047073
1993-01-01 415 0.043899
1993-03-01 412 0.043582
1993-07-01 404 0.042735
2002-03-01 402 0.042524
1997-03-01 398 0.042101
1996-07-01 379 0.040091
1998-06-01 377 0.039879
2004-09-03 370 0.039139
1994-08-01 369 0.039033
2000-06-01 366 0.038716
1993-02-01 354 0.037446
1999-04-01 343 0.036283
2020-11-04 340 0.035966
1994-05-01 332 0.035119
2002-09-01 332 0.035119
1994-12-01 329 0.034802
2003-09-01 329 0.034802
1993-12-01 325 0.034379
2003-02-01 323 0.034167
2000-07-01 320 0.033850
1997-11-01 312 0.033004
1995-05-01 297 0.031417
2003-07-01 287 0.030359
2002-10-01 279 0.029513
1999-08-01 278 0.029407
1995-12-01 269 0.028455
2001-11-01 263 0.027820
1995-11-01 259 0.027397
1995-09-01 246 0.026022
1998-08-01 245 0.025916
1994-11-01 240 0.025387
2001-05-01 237 0.025070
2020-10-02 233 0.024647
1997-12-01 228 0.024118
1992-08-01 226 0.023906
1998-07-01 224 0.023695
1996-05-01 223 0.023589
1994-01-11 219 0.023166
1997-06-01 216 0.022849
1996-08-01 198 0.020945
2000-04-05 197 0.020839
1997-05-01 196 0.020733
2001-09-01 196 0.020733
1993-06-01 195 0.020627
1996-09-01 195 0.020627
2001-10-01 192 0.020310
1993-10-01 189 0.019993
1992-07-01 185 0.019569
1993-05-01 184 0.019464
2000-12-01 179 0.018935
1997-04-01 174 0.018406
1997-07-01 166 0.017560
2000-08-01 157 0.016608
1996-11-01 151 0.015973
1999-09-01 150 0.015867
1999-11-01 150 0.015867
1999-10-01 147 0.015550
2017-07-01 144 0.015232
2000-03-09 141 0.014915
2015-01-01 140 0.014809
2017-01-01 140 0.014809
2014-12-01 139 0.014704
1993-04-01 139 0.014704
1995-08-01 138 0.014598
2015-02-01 137 0.014492
2002-06-17 134 0.014175
2001-06-01 129 0.013646
2000-11-01 128 0.013540
1996-10-01 126 0.013328
2015-12-01 126 0.013328
2015-06-01 124 0.013117
2000-09-01 123 0.013011
1991-01-20 120 0.012694
1992-09-01 120 0.012694
2014-01-01 118 0.012482
2017-05-01 117 0.012376
1997-08-01 117 0.012376
1992-10-01 115 0.012165
2015-05-01 115 0.012165
2013-12-01 115 0.012165
2015-03-01 115 0.012165
2000-10-01 114 0.012059
2002-06-12 112 0.011847
1993-08-01 112 0.011847
2016-01-01 108 0.011424
2016-05-01 107 0.011319
2021-02-05 106 0.011213
1998-10-01 106 0.011213
1997-09-01 103 0.010895
2002-06-13 102 0.010790
2015-08-01 101 0.010684
2014-07-01 98 0.010367
2014-05-01 97 0.010261
2015-11-01 97 0.010261
2014-09-01 94 0.009943
2017-02-01 91 0.009626
2017-04-01 91 0.009626
2018-01-01 88 0.009309
2010-12-01 88 0.009309
2016-11-01 87 0.009203
2018-07-01 87 0.009203
2017-03-01 87 0.009203
2011-05-01 86 0.009097
2014-06-01 85 0.008991
2014-11-01 84 0.008886
2009-02-01 82 0.008674
2020-03-01 82 0.008674
2014-03-01 81 0.008568
2012-07-01 81 0.008568
2013-09-01 81 0.008568
2018-12-01 80 0.008462
2013-08-01 80 0.008462
1993-09-01 78 0.008251
2018-04-01 77 0.008145
2014-04-01 77 0.008145
2012-04-01 76 0.008039
2002-06-07 76 0.008039
2011-01-01 74 0.007828
2014-10-01 74 0.007828
2010-09-01 74 0.007828
1997-10-01 73 0.007722
2017-12-01 73 0.007722
2010-05-01 71 0.007510
2012-12-01 71 0.007510
1991-02-20 71 0.007510
2017-10-01 70 0.007405
2012-05-01 70 0.007405
2002-06-14 69 0.007299
2002-06-11 68 0.007193
2013-11-01 68 0.007193
2013-10-01 67 0.007087
2013-06-01 67 0.007087
2017-11-01 66 0.006982
2016-10-01 65 0.006876
2018-09-01 65 0.006876
2002-06-06 65 0.006876
2002-07-05 64 0.006770
2008-02-01 64 0.006770
2013-01-01 63 0.006664
2011-09-01 63 0.006664
2014-02-01 63 0.006664
2012-01-01 62 0.006558
2018-05-01 61 0.006453
2012-09-01 60 0.006347
2019-12-01 60 0.006347
2020-02-01 59 0.006241
2012-06-01 58 0.006135
2011-08-01 58 0.006135
2013-05-01 57 0.006030
2010-01-01 56 0.005924
2008-03-01 56 0.005924
2002-06-19 56 0.005924
1991-12-01 54 0.005712
2019-06-01 54 0.005712
2010-11-01 53 0.005606
2013-04-01 53 0.005606
2007-02-01 53 0.005606
2002-06-10 52 0.005501
2010-08-01 52 0.005501
1992-01-02 51 0.005395
2018-11-01 51 0.005395
2019-11-01 51 0.005395
2019-01-01 51 0.005395
2008-04-01 50 0.005289
2019-09-01 50 0.005289
2008-06-01 50 0.005289
2009-03-01 49 0.005183
2020-04-01 49 0.005183
2011-11-01 47 0.004972
1991-08-01 46 0.004866
2019-05-01 46 0.004866
2020-01-01 45 0.004760
1991-01-21 43 0.004549
1991-11-15 42 0.004443
2012-11-01 42 0.004443
1991-07-01 42 0.004443
2009-09-01 41 0.004337
2008-10-01 40 0.004231
2008-07-01 40 0.004231
2009-01-01 40 0.004231
1991-11-06 40 0.004231
2010-06-01 39 0.004125
2009-08-01 39 0.004125
1991-11-01 39 0.004125
2009-04-01 38 0.004020
2018-10-01 38 0.004020
2007-03-01 38 0.004020
2005-09-01 37 0.003914
1991-11-11 36 0.003808
2009-10-01 35 0.003702
2011-10-01 35 0.003702
2008-01-01 34 0.003597
2009-06-01 33 0.003491
2009-11-01 33 0.003491
2007-01-01 33 0.003491
2006-05-01 33 0.003491
2002-06-18 33 0.003491
2006-04-01 32 0.003385
1992-03-02 32 0.003385
2020-05-01 32 0.003385
2002-07-04 32 0.003385
2009-12-01 32 0.003385
2005-12-01 32 0.003385
2005-08-01 31 0.003279
2006-12-01 30 0.003173
2002-06-28 30 0.003173
2007-10-01 30 0.003173
2007-06-01 29 0.003068
2008-05-01 29 0.003068
2008-08-01 29 0.003068
2007-04-01 29 0.003068
2002-06-21 28 0.002962
1992-06-02 28 0.002962
2009-05-01 27 0.002856
1992-02-02 26 0.002750
2006-07-01 26 0.002750
1991-06-03 25 0.002645
1992-01-14 25 0.002645
1995-01-02 25 0.002645
2007-12-01 25 0.002645
1994-10-06 25 0.002645
2010-10-01 24 0.002539
1991-10-01 24 0.002539
2005-10-01 24 0.002539
1994-03-28 23 0.002433
2007-11-01 23 0.002433
2009-07-01 23 0.002433
2006-08-01 23 0.002433
2007-08-01 23 0.002433
1991-03-25 22 0.002327
2019-10-01 22 0.002327
1995-10-02 22 0.002327
2006-06-01 22 0.002327
2006-10-01 20 0.002116
2005-11-01 20 0.002116
2006-09-01 19 0.002010
2007-09-01 19 0.002010
2007-05-01 19 0.002010
1993-04-05 16 0.001692
2008-11-01 15 0.001587
1992-02-01 14 0.001481
1991-09-01 13 0.001375
1993-11-08 13 0.001375
1994-07-04 13 0.001375
2007-07-01 13 0.001375
1993-10-04 13 0.001375
1996-01-02 12 0.001269
1994-03-04 12 0.001269
1992-04-01 12 0.001269
2006-11-01 12 0.001269
1993-12-07 11 0.001164
1994-01-07 11 0.001164
2002-07-03 10 0.001058
2006-01-01 10 0.001058
1992-12-14 10 0.001058
1992-09-30 10 0.001058
1993-01-07 9 0.000952
1993-09-16 8 0.000846
1993-03-11 8 0.000846
1991-05-16 7 0.000740
2020-06-01 7 0.000740
2002-07-01 6 0.000635
2021-12-01 6 0.000635
1992-01-16 6 0.000635
1995-09-07 5 0.000529
1994-02-07 5 0.000529
1996-09-02 5 0.000529
1994-06-06 5 0.000529
2021-06-01 5 0.000529
1993-02-12 5 0.000529
2002-06-05 5 0.000529
1993-08-05 5 0.000529
1991-01-10 4 0.000423
1990-12-10 4 0.000423
2020-08-01 4 0.000423
1995-05-02 4 0.000423
2021-07-01 4 0.000423
2021-11-02 4 0.000423
2021-04-01 3 0.000317
2002-06-27 3 0.000317
2002-06-25 3 0.000317
2002-06-26 3 0.000317
1991-04-02 3 0.000317
1990-10-01 3 0.000317
2022-11-02 3 0.000317
1991-01-01 3 0.000317
2020-10-05 3 0.000317
1991-05-08 3 0.000317
1992-09-25 3 0.000317
2002-07-02 3 0.000317
1992-10-14 3 0.000317
1996-08-02 3 0.000317
1993-04-12 3 0.000317
1996-09-30 2 0.000212
1993-02-17 2 0.000212
1994-12-04 2 0.000212
1995-10-21 2 0.000212
1997-05-04 2 0.000212
2002-05-28 2 0.000212
1999-12-28 2 0.000212
1998-08-18 2 0.000212
2002-06-01 2 0.000212
2021-08-02 2 0.000212
1990-03-01 2 0.000212
1990-02-20 2 0.000212
1990-09-25 2 0.000212
1998-08-21 2 0.000212
1993-02-09 2 0.000212
1994-08-19 2 0.000212
1998-12-18 2 0.000212
1991-10-25 2 0.000212
1996-12-21 2 0.000212
2022-01-03 2 0.000212
2002-06-03 2 0.000212
1993-07-05 2 0.000212
2002-07-19 1 0.000106
2002-07-12 1 0.000106
2002-07-28 1 0.000106
2002-06-04 1 0.000106
2002-07-10 1 0.000106
1992-09-23 1 0.000106
1992-05-18 1 0.000106
2023-06-01 1 0.000106
1996-09-05 1 0.000106
1991-03-15 1 0.000106
1996-06-03 1 0.000106
2021-02-01 1 0.000106
1991-10-21 1 0.000106
1990-01-01 1 0.000106
1994-10-10 1 0.000106
1992-09-19 1 0.000106
1996-07-12 1 0.000106
1994-07-28 1 0.000106
1999-04-22 1 0.000106
2021-05-03 1 0.000106
1996-03-20 1 0.000106
1996-12-26 1 0.000106
2000-02-14 1 0.000106
2000-03-30 1 0.000106
1991-06-01 1 0.000106
2014-04-05 1 0.000106
2000-04-26 1 0.000106
1990-01-10 1 0.000106
1991-01-08 1 0.000106
1994-01-10 1 0.000106
1991-04-01 1 0.000106
1996-12-28 1 0.000106
1997-06-04 1 0.000106
1992-08-14 1 0.000106
1992-05-15 1 0.000106
1990-04-20 1 0.000106
1994-02-14 1 0.000106
1991-09-03 1 0.000106
1991-01-31 1 0.000106
1992-03-24 1 0.000106
1995-07-19 1 0.000106
1992-06-10 1 0.000106
1992-04-02 1 0.000106
1990-11-01 1 0.000106
1996-09-25 1 0.000106
2002-07-17 1 0.000106
2002-07-27 1 0.000106
1993-04-03 1 0.000106
1994-10-11 1 0.000106
1990-01-20 1 0.000106
1993-02-15 1 0.000106
1999-03-25 1 0.000106
1993-05-04 1 0.000106
1993-02-05 1 0.000106
1994-09-10 1 0.000106
1993-02-04 1 0.000106
1993-02-08 1 0.000106
1997-04-02 1 0.000106
1999-08-12 1 0.000106
2000-03-28 1 0.000106
1990-06-10 1 0.000106
1991-04-23 1 0.000106
1998-10-20 1 0.000106
1992-03-01 1 0.000106
1993-04-19 1 0.000106
1993-03-10 1 0.000106
1991-05-05 1 0.000106
1998-02-05 1 0.000106
1999-10-25 1 0.000106
1991-04-04 1 0.000106
1998-02-09 1 0.000106
2022-06-01 1 0.000106
1993-04-30 1 0.000106
1999-10-27 1 0.000106
1995-10-11 1 0.000106
1990-03-31 1 0.000106
1992-05-21 1 0.000106
1995-10-26 1 0.000106
1996-11-09 1 0.000106
1992-09-29 1 0.000106
1991-08-03 1 0.000106
1992-11-02 1 0.000106
1991-03-20 1 0.000106
2022-03-09 1 0.000106
2022-05-02 1 0.000106
2023-04-03 1 0.000106
2022-07-04 1 0.000106
1992-05-22 1 0.000106
1993-03-08 1 0.000106
1991-06-20 1 0.000106
2020-12-01 1 0.000106
1991-01-04 1 0.000106
1990-02-12 1 0.000106
1991-12-03 1 0.000106
1996-12-20 1 0.000106
1992-06-08 1 0.000106
1996-12-13 1 0.000106
1992-11-19 1 0.000106
1993-03-16 1 0.000106
1992-02-03 1 0.000106
1996-01-30 1 0.000106
msf_datefirstrecurringdonorquota__c: Fecha de la primera donacion recurrente.
Se puede observar quehay un 47%.
Analsis de distribución por variables
-> msf_datelastrecurringdonorquota__c: Variable fecha
In [169]:
# Vamos a realizar analisis por cada variable
var = "msf_datelastrecurringdonorquota__c"
In [170]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datelastrecurringdonorquota__c es 858069. Lo que supone un 47.58012419742722%
El nº de vacios para la variable msf_datelastrecurringdonorquota__c es 0. Lo que supone un 0.0%
Out[170]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c']
In [171]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[171]:
# Tot % Tot
2023-07-04 393458 41.620352
2023-06-02 22387 2.368118
2023-05-03 21046 2.226265
2023-01-03 14510 1.534881
2023-02-02 13748 1.454276
2023-03-02 11212 1.186016
2022-12-02 10506 1.111334
2023-04-04 9691 1.025123
2022-11-03 8475 0.896493
2022-10-04 7566 0.800338
2022-08-02 7167 0.758132
2020-09-01 6699 0.708626
2022-09-02 6368 0.673613
2018-02-01 5264 0.556831
2018-01-03 4175 0.441635
2021-04-02 3879 0.410324
2022-01-04 3867 0.409055
2017-12-04 3853 0.407574
2019-01-02 3847 0.406939
2018-12-03 3757 0.397419
2021-01-05 3666 0.387793
2019-12-02 3651 0.386206
2021-12-02 3630 0.383985
2021-07-02 3591 0.379859
2021-10-02 3581 0.378802
2018-03-01 3569 0.377532
2021-11-03 3438 0.363675
2021-06-02 3421 0.361877
2022-07-05 3410 0.360713
2022-05-03 3400 0.359655
2021-03-02 3396 0.359232
2021-05-04 3395 0.359126
2018-09-03 3323 0.351510
2022-02-02 3290 0.348019
2021-02-02 3277 0.346644
2018-10-02 3275 0.346433
2020-01-02 3258 0.344634
2022-04-02 3253 0.344105
2018-04-03 3230 0.341672
2018-07-02 3223 0.340932
2022-06-02 3201 0.338605
2019-02-01 3144 0.332575
2021-08-03 3143 0.332469
2020-02-03 3126 0.330671
2017-10-02 3114 0.329402
2019-10-02 3114 0.329402
2018-08-01 3107 0.328661
2020-03-02 3099 0.327815
2019-08-01 3098 0.327709
2022-03-02 3095 0.327392
2018-06-01 3075 0.325276
2019-09-02 3063 0.324007
2017-11-02 3032 0.320728
2018-11-02 3027 0.320199
2019-07-01 3024 0.319882
2019-03-01 2966 0.313746
2017-01-02 2947 0.311736
2016-12-01 2909 0.307717
2018-05-03 2882 0.304861
2017-09-01 2868 0.303380
2019-04-01 2811 0.297350
2019-11-04 2788 0.294917
2021-09-02 2681 0.283599
2019-05-02 2640 0.279262
2017-08-01 2596 0.274607
2019-06-03 2538 0.268472
2017-07-03 2517 0.266251
2015-12-02 2422 0.256201
2016-10-03 2399 0.253768
2017-02-02 2394 0.253240
2017-03-02 2383 0.252076
2017-05-02 2360 0.249643
2020-04-02 2339 0.247422
2017-06-01 2336 0.247104
2016-02-01 2269 0.240017
2017-04-03 2269 0.240017
2016-11-02 2265 0.239594
2012-12-03 2252 0.238219
2016-01-04 2217 0.234516
2016-09-01 2209 0.233670
2020-07-01 2190 0.231660
2015-01-02 2138 0.226160
2012-01-02 2137 0.226054
2014-01-02 2131 0.225419
2014-12-02 2112 0.223409
2020-05-03 2102 0.222352
2016-08-01 2088 0.220871
2015-10-01 2083 0.220342
2016-07-01 2081 0.220130
2020-06-02 2066 0.218543
2013-01-02 2053 0.217168
2016-03-01 2029 0.214630
2012-10-01 2027 0.214418
2016-04-01 2026 0.214312
2012-02-01 2020 0.213677
2015-09-01 2014 0.213043
2011-12-01 2009 0.212514
2016-06-01 2003 0.211879
2013-02-01 1981 0.209552
2015-02-02 1980 0.209446
2015-03-02 1975 0.208917
2015-11-03 1971 0.208494
2015-04-01 1963 0.207648
2014-02-03 1962 0.207542
2016-05-02 1924 0.203523
2013-12-02 1909 0.201936
2015-07-01 1898 0.200772
2015-06-02 1880 0.198868
2013-03-01 1831 0.193685
2015-08-03 1819 0.192416
2012-03-01 1807 0.191146
2012-04-02 1805 0.190935
2013-10-02 1787 0.189031
2012-05-03 1787 0.189031
2012-11-02 1780 0.188290
2014-03-03 1775 0.187761
2020-11-04 1766 0.186809
2014-10-02 1761 0.186280
2012-09-03 1761 0.186280
2012-07-02 1753 0.185434
2015-05-04 1730 0.183001
2013-04-02 1709 0.180780
2013-09-02 1666 0.176231
2014-09-03 1655 0.175067
2014-04-02 1632 0.172634
2012-08-01 1632 0.172634
2014-05-05 1627 0.172106
2014-08-01 1623 0.171682
2014-07-02 1601 0.169355
2014-11-03 1599 0.169144
2020-10-02 1593 0.168509
2013-07-01 1581 0.167240
2010-12-02 1537 0.162585
2011-01-03 1536 0.162480
2011-03-01 1530 0.161845
2013-05-02 1519 0.160681
2012-06-04 1518 0.160575
2011-11-02 1480 0.156556
2014-06-05 1471 0.155604
2020-12-02 1437 0.152007
2011-10-04 1432 0.151478
2011-09-02 1428 0.151055
2011-02-01 1422 0.150420
2013-11-04 1418 0.149997
2011-07-01 1386 0.146612
2011-06-01 1368 0.144708
2013-08-02 1362 0.144074
2011-04-01 1350 0.142804
2013-06-03 1342 0.141958
2011-05-02 1273 0.134659
2009-02-03 1268 0.134130
2009-01-02 1245 0.131697
2011-08-02 1238 0.130957
2010-10-04 1215 0.128524
2008-12-01 1205 0.127466
2008-10-02 1187 0.125562
2010-09-02 1180 0.124821
2009-12-02 1179 0.124716
2010-11-02 1173 0.124081
2008-09-01 1155 0.122177
2010-08-02 1097 0.116042
2010-07-01 1080 0.114243
2008-01-03 1066 0.112762
2010-05-03 1066 0.112762
2008-03-03 1042 0.110224
2010-06-02 1041 0.110118
2020-08-03 1034 0.109377
2010-04-01 1034 0.109377
2009-03-03 1031 0.109060
2010-02-01 1026 0.108531
2008-02-04 1026 0.108531
2010-03-01 1024 0.108320
2009-04-02 1023 0.108214
2010-01-04 1022 0.108108
2009-10-02 1021 0.108002
2007-12-02 982 0.103877
2009-11-02 967 0.102290
2008-11-03 945 0.099963
2008-07-04 945 0.099963
2009-09-02 938 0.099223
2008-05-02 926 0.097953
2009-05-04 895 0.094674
2008-04-04 894 0.094568
2008-06-02 866 0.091606
2007-04-02 863 0.091289
2009-06-04 846 0.089491
2009-07-02 842 0.089068
2007-10-04 840 0.088856
2007-11-02 836 0.088433
2007-07-04 806 0.085259
2007-09-03 804 0.085048
2007-01-04 772 0.081663
2007-03-02 768 0.081240
2009-08-03 760 0.080394
2008-08-08 749 0.079230
2007-02-05 716 0.075739
2007-05-04 702 0.074258
2007-08-02 698 0.073835
2007-06-05 646 0.068334
2002-05-01 643 0.068017
2006-10-02 627 0.066325
2006-02-03 601 0.063574
2006-01-05 597 0.063151
2006-12-02 571 0.060401
2006-09-04 541 0.057227
2005-12-03 532 0.056275
2006-06-02 521 0.055112
2006-03-03 521 0.055112
2006-04-03 520 0.055006
2006-07-03 518 0.054795
2006-11-03 518 0.054795
2006-05-04 507 0.053631
2006-08-02 494 0.052256
2005-10-03 448 0.047390
2005-04-04 441 0.046649
2005-09-02 439 0.046438
2005-03-04 428 0.045274
2005-08-02 423 0.044745
2005-02-04 418 0.044216
2005-11-03 406 0.042947
2005-05-04 396 0.041889
2005-01-04 390 0.041255
2005-06-03 385 0.040726
2004-09-03 380 0.040197
2005-07-04 357 0.037764
2004-02-01 355 0.037552
2004-03-01 328 0.034696
2004-01-01 322 0.034061
2004-07-01 321 0.033956
2004-10-06 315 0.033321
2004-04-01 307 0.032475
2003-01-01 286 0.030253
2004-11-04 286 0.030253
2003-02-01 277 0.029301
2004-12-05 273 0.028878
2000-10-01 267 0.028244
2004-05-01 263 0.027820
2002-10-01 254 0.026868
2001-10-01 250 0.026445
2001-02-01 242 0.025599
2003-04-01 236 0.024964
2004-06-01 236 0.024964
2003-12-01 235 0.024859
2002-01-01 233 0.024647
2003-10-01 215 0.022743
2002-09-01 207 0.021897
2003-07-01 206 0.021791
2003-09-01 206 0.021791
2004-08-01 205 0.021685
2003-03-01 203 0.021474
2001-07-01 200 0.021156
2003-11-01 199 0.021050
1996-10-01 194 0.020521
2001-04-01 187 0.019781
2003-06-01 187 0.019781
2002-08-01 186 0.019675
2001-01-01 185 0.019569
2003-05-01 182 0.019252
2001-03-01 178 0.018829
2003-08-01 176 0.018617
2001-12-01 171 0.018089
2002-12-01 170 0.017983
2002-11-01 168 0.017771
2002-02-01 165 0.017454
1997-10-01 162 0.017137
1997-01-01 162 0.017137
2000-12-01 157 0.016608
2000-04-05 155 0.016396
2000-07-01 154 0.016290
1998-02-01 154 0.016290
2002-04-01 153 0.016184
2001-09-01 152 0.016079
2001-11-01 151 0.015973
1997-02-01 148 0.015656
1995-10-02 146 0.015444
1999-02-01 146 0.015444
2000-02-01 145 0.015338
2002-03-01 145 0.015338
1997-04-01 143 0.015127
1996-04-01 143 0.015127
1999-01-01 136 0.014386
1996-07-01 133 0.014069
2000-09-01 131 0.013857
1996-02-01 130 0.013752
2001-06-01 130 0.013752
1999-07-01 129 0.013646
1997-07-01 129 0.013646
2000-05-01 124 0.013117
1999-04-01 123 0.013011
1998-01-01 122 0.012905
2001-08-01 121 0.012799
1998-10-01 120 0.012694
1998-04-01 120 0.012694
2000-08-01 118 0.012482
2001-05-01 116 0.012271
2000-11-01 114 0.012059
2000-06-01 109 0.011530
2017-07-01 108 0.011424
1998-03-01 108 0.011424
1996-01-02 107 0.011319
2000-01-13 107 0.011319
1995-02-01 107 0.011319
1999-10-01 107 0.011319
2002-07-10 104 0.011001
2000-03-09 100 0.010578
1999-12-01 99 0.010472
1998-07-01 97 0.010261
1995-07-01 97 0.010261
1995-04-01 91 0.009626
1999-03-01 90 0.009520
1998-12-01 89 0.009415
1997-03-01 85 0.008991
2002-07-13 83 0.008780
1995-01-02 83 0.008780
1999-06-01 82 0.008674
1999-09-01 75 0.007934
2017-01-01 73 0.007722
1996-03-01 73 0.007722
2017-12-01 71 0.007510
1999-08-01 70 0.007405
1996-12-01 68 0.007193
1999-11-01 68 0.007193
2015-12-01 67 0.007087
2002-06-13 66 0.006982
2017-02-01 66 0.006982
2018-01-01 66 0.006982
2015-02-01 65 0.006876
2017-05-01 64 0.006770
2017-03-01 63 0.006664
2015-05-01 63 0.006664
2017-11-01 61 0.006453
2016-05-01 61 0.006453
2015-08-01 61 0.006453
1995-11-01 59 0.006241
1997-06-01 59 0.006241
2015-06-01 59 0.006241
1998-09-01 58 0.006135
1995-03-01 57 0.006030
2016-10-01 57 0.006030
2015-11-01 56 0.005924
1998-06-01 55 0.005818
2015-03-01 55 0.005818
1996-09-02 55 0.005818
1996-11-01 54 0.005712
2016-01-01 53 0.005606
2017-04-01 52 0.005501
2015-01-01 52 0.005501
1994-10-06 52 0.005501
2014-03-01 52 0.005501
1995-06-01 52 0.005501
1999-05-01 52 0.005501
2018-10-01 51 0.005395
2014-12-01 50 0.005289
2017-10-01 50 0.005289
2016-11-01 50 0.005289
1998-08-01 50 0.005289
2014-09-01 50 0.005289
2013-12-01 49 0.005183
2018-04-01 48 0.005077
2013-09-01 48 0.005077
1995-12-01 48 0.005077
2018-07-01 47 0.004972
1998-11-01 47 0.004972
2014-05-01 46 0.004866
1997-09-01 46 0.004866
2012-05-01 45 0.004760
1994-10-01 45 0.004760
1997-11-01 45 0.004760
2009-02-01 45 0.004760
2012-04-01 44 0.004654
2018-12-01 44 0.004654
1997-08-01 43 0.004549
2019-12-01 42 0.004443
1996-06-03 42 0.004443
2018-11-01 41 0.004337
1997-05-01 40 0.004231
1997-12-01 40 0.004231
1994-07-04 39 0.004125
2013-11-01 39 0.004125
1996-05-01 38 0.004020
2012-07-01 38 0.004020
1995-09-07 38 0.004020
1998-05-01 37 0.003914
2020-02-01 37 0.003914
1994-11-01 37 0.003914
2014-10-01 37 0.003914
1994-02-01 36 0.003808
2014-07-01 35 0.003702
2012-09-01 35 0.003702
2014-11-01 35 0.003702
2008-10-01 35 0.003702
1996-08-02 34 0.003597
1994-12-04 34 0.003597
2014-04-01 34 0.003597
2013-10-01 33 0.003491
2014-06-01 33 0.003491
2011-05-01 33 0.003491
1994-03-28 32 0.003385
2010-12-01 31 0.003279
2020-03-01 31 0.003279
2019-05-01 31 0.003279
2020-01-01 31 0.003279
2009-03-01 30 0.003173
2019-09-01 30 0.003173
1994-01-07 30 0.003173
2013-05-01 30 0.003173
2014-02-01 30 0.003173
2018-05-01 29 0.003068
2002-06-05 29 0.003068
2013-01-01 28 0.002962
2018-09-01 28 0.002962
2019-11-01 28 0.002962
2012-01-01 28 0.002962
1995-05-02 27 0.002856
2009-04-01 27 0.002856
2019-01-01 27 0.002856
2011-01-01 27 0.002856
2020-04-01 27 0.002856
2012-11-01 27 0.002856
1993-07-01 27 0.002856
1993-04-05 26 0.002750
2009-09-01 26 0.002750
2002-06-08 26 0.002750
2009-08-01 26 0.002750
2009-01-01 26 0.002750
2011-11-01 25 0.002645
2012-12-01 25 0.002645
2010-05-01 25 0.002645
2011-10-01 25 0.002645
2011-08-01 25 0.002645
1993-10-04 24 0.002539
2013-04-01 24 0.002539
2008-06-01 24 0.002539
2008-04-01 24 0.002539
2019-10-01 23 0.002433
2013-06-01 23 0.002433
2008-02-01 23 0.002433
2020-06-01 22 0.002327
2020-05-01 22 0.002327
1993-07-05 22 0.002327
1995-08-01 22 0.002327
2010-11-01 21 0.002221
2019-06-01 20 0.002116
2008-07-01 20 0.002116
2009-10-01 20 0.002116
2014-01-01 20 0.002116
2011-09-01 20 0.002116
2008-11-01 20 0.002116
2012-06-01 20 0.002116
2008-03-01 19 0.002010
2007-11-01 19 0.002010
2013-08-01 19 0.002010
2005-12-01 18 0.001904
2010-06-01 18 0.001904
1992-06-01 18 0.001904
2009-06-01 18 0.001904
1995-10-01 18 0.001904
2010-01-01 17 0.001798
2010-09-01 17 0.001798
2021-02-05 17 0.001798
2009-05-01 17 0.001798
2007-08-01 17 0.001798
1994-02-07 17 0.001798
2008-08-01 17 0.001798
2010-08-01 17 0.001798
1992-11-01 17 0.001798
2006-04-01 16 0.001692
1993-03-01 16 0.001692
2009-12-01 16 0.001692
1993-11-08 16 0.001692
1994-09-01 16 0.001692
1994-07-01 15 0.001587
1994-03-04 15 0.001587
2008-05-01 15 0.001587
2006-11-01 15 0.001587
2006-07-01 15 0.001587
1993-01-07 14 0.001481
1992-12-01 14 0.001481
1993-01-01 14 0.001481
1995-01-01 14 0.001481
2000-01-01 14 0.001481
2007-10-01 13 0.001375
2007-03-01 13 0.001375
2005-08-01 13 0.001375
2008-01-01 13 0.001375
1993-05-01 13 0.001375
1996-06-01 13 0.001375
1994-03-01 13 0.001375
2009-07-01 13 0.001375
2006-10-01 12 0.001269
1996-01-01 12 0.001269
2006-05-01 12 0.001269
2000-03-01 12 0.001269
2007-04-01 11 0.001164
2006-09-01 11 0.001164
2010-10-01 11 0.001164
2006-06-01 11 0.001164
1993-08-05 10 0.001058
2007-09-01 10 0.001058
2007-06-01 10 0.001058
2007-01-01 10 0.001058
2009-11-01 10 0.001058
2007-12-01 10 0.001058
2005-09-01 9 0.000952
1993-11-01 9 0.000952
1994-01-01 9 0.000952
2005-11-01 9 0.000952
2023-06-01 9 0.000952
1994-06-06 9 0.000952
2007-02-01 8 0.000846
1992-07-01 8 0.000846
2022-12-01 8 0.000846
1993-12-07 8 0.000846
2020-08-01 8 0.000846
1994-08-01 8 0.000846
2023-07-03 8 0.000846
1993-02-17 8 0.000846
1992-04-02 7 0.000740
1994-05-09 7 0.000740
1993-09-16 7 0.000740
2007-07-01 7 0.000740
1992-10-14 7 0.000740
2007-05-01 7 0.000740
1992-12-14 6 0.000635
1993-12-01 6 0.000635
2006-12-01 6 0.000635
1992-01-02 6 0.000635
2006-08-01 6 0.000635
2005-10-01 6 0.000635
2022-11-02 6 0.000635
1995-05-01 5 0.000529
1991-01-20 5 0.000529
1996-08-01 5 0.000529
1995-09-01 5 0.000529
1994-06-01 5 0.000529
1993-02-01 4 0.000423
2022-06-01 4 0.000423
1991-01-21 4 0.000423
2002-07-11 4 0.000423
2022-09-01 4 0.000423
1994-04-01 4 0.000423
1994-12-01 4 0.000423
1993-03-08 4 0.000423
1991-12-01 4 0.000423
1992-09-01 3 0.000317
1992-10-01 3 0.000317
2006-01-01 3 0.000317
1993-08-01 3 0.000317
1991-07-01 3 0.000317
1992-08-14 3 0.000317
1996-09-01 3 0.000317
2022-07-04 3 0.000317
1991-02-20 3 0.000317
1993-04-27 3 0.000317
1991-09-01 3 0.000317
2002-06-18 2 0.000212
1991-08-01 2 0.000212
2021-05-03 2 0.000212
2022-01-03 2 0.000212
2023-05-02 2 0.000212
1992-02-02 2 0.000212
2020-10-05 2 0.000212
2023-04-03 2 0.000212
2022-04-01 2 0.000212
2021-06-01 2 0.000212
1994-05-01 2 0.000212
1992-08-01 2 0.000212
2022-05-02 2 0.000212
2021-04-01 2 0.000212
1992-06-02 2 0.000212
1993-04-01 2 0.000212
1992-05-21 2 0.000212
1992-09-30 2 0.000212
1993-09-01 2 0.000212
1991-04-15 2 0.000212
1993-03-11 2 0.000212
1993-02-12 2 0.000212
1991-11-06 2 0.000212
1990-10-01 1 0.000106
1991-11-01 1 0.000106
1990-12-20 1 0.000106
1991-01-10 1 0.000106
1991-06-03 1 0.000106
1997-05-04 1 0.000106
1991-10-01 1 0.000106
1994-07-28 1 0.000106
1992-02-05 1 0.000106
1999-09-14 1 0.000106
1991-09-03 1 0.000106
1993-04-30 1 0.000106
1992-03-02 1 0.000106
1993-04-19 1 0.000106
1991-01-04 1 0.000106
1999-08-12 1 0.000106
1992-04-01 1 0.000106
2023-01-02 1 0.000106
2018-10-08 1 0.000106
1991-03-25 1 0.000106
1993-06-01 1 0.000106
1993-05-04 1 0.000106
1990-11-01 1 0.000106
2021-08-02 1 0.000106
1993-04-03 1 0.000106
2022-03-09 1 0.000106
2002-06-11 1 0.000106
2021-12-01 1 0.000106
2021-07-01 1 0.000106
2022-08-01 1 0.000106
2021-04-20 1 0.000106
msf_datelastrecurringdonorquota__c: Fecha de la ultima donacion recurrente.
Se puede observar que hay un 47% de vacios.
Analsis de distribución por variables
-> msf_datelastdonation__c: Variable fecha
In [172]:
# Vamos a realizar analisis por cada variable
var = "msf_datelastdonation__c"
In [173]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datelastdonation__c es 1175962. Lo que supone un 65.20736445606927%
El nº de vacios para la variable msf_datelastdonation__c es 0. Lo que supone un 0.0%
Out[173]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c']
In [174]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[174]:
# Tot % Tot
2017-05-16 9885 1.575407
2022-12-02 7522 1.198807
2020-07-01 7461 1.189085
2023-03-02 6831 1.088680
2020-06-01 4537 0.723077
... ... ...
1994-03-23 1 0.000159
1991-05-04 1 0.000159
2002-08-23 1 0.000159
1999-05-02 1 0.000159
1994-09-18 1 0.000159

10773 rows × 2 columns

msf_datelastdonation__c: Fecha de la ultima donacion.
Se puede observar que 65% de vacios.
Analsis de distribución por variables
-> npsp__largest_soft_credit_date__c: Variable fecha
In [175]:
# Vamos a realizar analisis por cada variable
var = "npsp__largest_soft_credit_date__c"
In [176]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__largest_soft_credit_date__c es 1803419. Lo que supone un 100.0%
El nº de vacios para la variable npsp__largest_soft_credit_date__c es 0. Lo que supone un 0.0%
Out[176]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c']
In [177]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[177]:
# Tot % Tot
npsp__largest_soft_credit_date__c: Fecha de la aportacion indirecta más importante.
Se puede observar que tiene un 100% de vacios.
Analsis de distribución por variables
-> npsp__first_soft_credit_date__c: Variable fecha
In [178]:
# Vamos a realizar analisis por cada variable
var = "npsp__first_soft_credit_date__c"
In [179]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__first_soft_credit_date__c es 1803419. Lo que supone un 100.0%
El nº de vacios para la variable npsp__first_soft_credit_date__c es 0. Lo que supone un 0.0%
Out[179]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c']
In [180]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[180]:
# Tot % Tot
npsp__first_soft_credit_date__c: Fecha de la primera aportación indirecta.
Se puede observar que 100% vacios.
Analsis de distribución por variables
-> msf_entrydatecurrentrecurringdonor__c: Variable fecha
In [181]:
# Vamos a realizar analisis por cada variable
var = "msf_entrydatecurrentrecurringdonor__c"
In [182]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_entrydatecurrentrecurringdonor__c es 809767. Lo que supone un 44.90176714340927%
El nº de vacios para la variable msf_entrydatecurrentrecurringdonor__c es 0. Lo que supone un 0.0%
Out[182]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c']
In [183]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[183]:
# Tot % Tot
2000-02-01 3949 0.397423
2004-01-01 3842 0.386654
1994-10-01 3293 0.331404
2000-01-01 3274 0.329492
1995-02-01 2918 0.293664
... ... ...
2002-01-30 1 0.000101
2003-11-17 1 0.000101
2005-08-27 1 0.000101
2002-01-16 1 0.000101
2011-06-25 1 0.000101

7860 rows × 2 columns

msf_entrydatecurrentrecurringdonor__c: Fecha de la ultima entrada de socio.
Se puede observar que tiene menos del 44% de registros a vacio.
Analsis de distribución por variables
-> npsp__last_soft_credit_date__c: Variable fecha
In [184]:
# Vamos a realizar analisis por cada variable
var = "npsp__last_soft_credit_date__c"
In [185]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__last_soft_credit_date__c es 1803419. Lo que supone un 100.0%
El nº de vacios para la variable npsp__last_soft_credit_date__c es 0. Lo que supone un 0.0%
Out[185]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c']
In [186]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[186]:
# Tot % Tot
npsp__last_soft_credit_date__c: Fecha de la ultima aportación indirecta.
Se puede observar que tiene todos los registros como vacios.
Analsis de distribución por variables
-> msf_firstentrydaterecurringdonor__c: Variable fecha
In [187]:
# Vamos a realizar analisis por cada variable
var = "msf_firstentrydaterecurringdonor__c"
In [188]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstentrydaterecurringdonor__c es 809975. Lo que supone un 44.913300791441145%
El nº de vacios para la variable msf_firstentrydaterecurringdonor__c es 0. Lo que supone un 0.0%
Out[188]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c']
In [189]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[189]:
# Tot % Tot
2004-01-01 4974 0.500682
2000-02-01 4594 0.462432
1994-10-01 3823 0.384823
2000-01-01 3804 0.382910
1995-02-01 3374 0.339627
... ... ...
2003-02-04 1 0.000101
2003-07-11 1 0.000101
2004-08-12 1 0.000101
2003-01-07 1 0.000101
2010-04-24 1 0.000101

7926 rows × 2 columns

msf_firstentrydaterecurringdonor__c: Fecha de la primera entrada de socio.
Se puede observar que 44% vacios.
Analsis de distribución por variables
-> npo02__firstclosedate__c: Variable fecha
In [190]:
# Vamos a realizar analisis por cada variable
var = "npo02__firstclosedate__c"
In [191]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__firstclosedate__c es 506557. Lo que supone un 28.088702625402085%
El nº de vacios para la variable npo02__firstclosedate__c es 0. Lo que supone un 0.0%
Out[191]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c']
In [192]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[192]:
# Tot % Tot
2016-12-01 50715 3.910593
2015-12-02 50688 3.908511
2014-12-02 46621 3.594908
2017-12-04 45905 3.539698
2013-12-02 30590 2.358771
... ... ...
1999-12-19 1 0.000077
1992-02-26 1 0.000077
1995-12-10 1 0.000077
1990-02-02 1 0.000077
2013-04-14 1 0.000077

10973 rows × 2 columns

npo02__firstclosedate__c: Fecha de la primera donación de cualquier tipo.
Se puede observar que hay un 5% de vacios.
Analsis de distribución por variables
-> msf_lastrecurringdonationdate__c: Variable fecha
In [193]:
# Vamos a realizar analisis por cada variable
var = "msf_lastrecurringdonationdate__c"
In [194]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lastrecurringdonationdate__c es 1231036. Lo que supone un 68.2612304738943%
El nº de vacios para la variable msf_lastrecurringdonationdate__c es 0. Lo que supone un 0.0%
Out[194]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c']
In [195]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[195]:
# Tot % Tot
2020-03-12 2204 0.385057
2014-03-13 1942 0.339283
2023-05-10 1794 0.313426
2018-03-07 1616 0.282328
2018-04-09 1555 0.271671
... ... ...
2018-08-25 1 0.000175
2006-10-22 1 0.000175
1993-04-19 1 0.000175
2008-04-20 1 0.000175
2016-04-09 1 0.000175

7032 rows × 2 columns

msf_lastrecurringdonationdate__c: Fecha de la ultima baja de socio.
Se puede observar que existen un 42% de vacios. Lo que puede cuadrar con la informacion de donantes recurrentes activos de la tabla de donaciones recurrentes.
Analsis de distribución por variables
-> npo02__lastclosedate__c: Variable fecha
In [196]:
# Vamos a realizar analisis por cada variable
var = "npo02__lastclosedate__c"
In [197]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__lastclosedate__c es 1803419. Lo que supone un 100.0%
El nº de vacios para la variable npo02__lastclosedate__c es 0. Lo que supone un 0.0%
Out[197]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c']
In [198]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[198]:
# Tot % Tot
npo02__lastclosedate__c: Fecha de la ultima donación de cualquier tipo.
Se puede observar que tdos los registros están a vacios.
Analsis de distribución por variables
-> gender__c: Variable categorica
In [199]:
# Vamos a realizar analisis por cada variable
var = "gender__c"
In [200]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable gender__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable gender__c es 139747. Lo que supone un 7.749003420724746%
In [201]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[201]:
# Tot % Tot
Female 793486 43.998982
Male 605658 33.583876
Other 264425 14.662427
139747 7.749003
M 66 0.003660
H 37 0.002052
gender__c: Genero.
Se puede observar que existe un 7% de registros a vacio.
Analsis de distribución por variables
-> msf_languagepreferer__c: Variable categorica
In [202]:
# Vamos a realizar analisis por cada variable
var = "msf_languagepreferer__c"
In [203]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_languagepreferer__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_languagepreferer__c es 1. Lo que supone un 5.545023092248668e-05%
In [204]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[204]:
# Tot % Tot
ESP 1607878 89.157206
CAT 178609 9.903910
GAL 11099 0.615442
EUS 5805 0.321889
ING 27 0.001497
1 0.000055
msf_languagepreferer__c: lenguaje de comunicacion.
Se puede observar que no tiene vacios, casi todos los casos son Español, y en menor medida Catalan.
Analsis de distribución por variables
-> npo02__largestamount__c: Variable numerica
In [205]:
# Vamos a realizar analisis por cada variable
var = "npo02__largestamount__c"
In [206]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__largestamount__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__largestamount__c es 0. Lo que supone un 0.0%
In [207]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[207]:
# Tot % Tot
0.0 1803419 100.0
npo02__largestamount__c: importe de la donacion más grande.
Se puede observar que aunque no tenga vacios, siempre se informa a 0.
Analsis de distribución por variables
-> npo02__smallestamount__c: Variable numerica
In [208]:
# Vamos a realizar analisis por cada variable
var = "npo02__smallestamount__c"
In [209]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__smallestamount__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__smallestamount__c es 0. Lo que supone un 0.0%
In [210]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[210]:
# Tot % Tot
0.0 1803419 100.0
npo02__smallestamount__c: importe de la donacion más pequeña.
Se puede observar que aunque no tenga vacios, la variable siempre está informada a 0.
Analsis de distribución por variables
-> npsp__first_soft_credit_amount__c: Variable numerica
In [211]:
# Vamos a realizar analisis por cada variable
var = "npsp__first_soft_credit_amount__c"
In [212]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__first_soft_credit_amount__c es 1803419. Lo que supone un 100.0%
El nº de vacios para la variable npsp__first_soft_credit_amount__c es 0. Lo que supone un 0.0%
Out[212]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c']
In [213]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[213]:
# Tot % Tot
npsp__first_soft_credit_amount__c: importe de la primera aportacion indirecta.
Se puede observar que esta variable es nula en su totalidad.
Analsis de distribución por variables
-> npo02__lastoppamount__c: Variable numerica
In [214]:
# Vamos a realizar analisis por cada variable
var = "npo02__lastoppamount__c"
In [215]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__lastoppamount__c es 3626. Lo que supone un 0.2010625373249367%
El nº de vacios para la variable npo02__lastoppamount__c es 0. Lo que supone un 0.0%
In [216]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[216]:
# Tot % Tot
0.00 502942 27.944436
10.00 162997 9.056430
15.00 97374 5.410289
20.00 94104 5.228601
30.00 71623 3.979513
... ... ...
78.28 1 0.000056
1674.76 1 0.000056
55.80 1 0.000056
275.03 1 0.000056
122.12 1 0.000056

10069 rows × 2 columns

npo02__lastoppamount__c: importe de la ultima aportacion.
Se puede observar que casi no tiene vacios, para los donantes recurrentes casi 100% de los casos coincidirá con la donacion de socio, por lo que se descarta.
Analsis de distribución por variables
-> npsp__last_soft_credit_amount__c: Variable numerica
In [217]:
# Vamos a realizar analisis por cada variable
var = "npsp__last_soft_credit_amount__c"
In [218]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__last_soft_credit_amount__c es 1803419. Lo que supone un 100.0%
El nº de vacios para la variable npsp__last_soft_credit_amount__c es 0. Lo que supone un 0.0%
Out[218]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c']
In [219]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[219]:
# Tot % Tot
npsp__last_soft_credit_amount__c: importe de la ultima aportacion indirecta.
Se puede observar que es nula.
Analsis de distribución por variables
-> msf_annualizedquotachange__c: Variable numerica
In [220]:
# Vamos a realizar analisis por cada variable
var = "msf_annualizedquotachange__c"
In [221]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_annualizedquotachange__c es 1145924. Lo que supone un 63.541750419619625%
El nº de vacios para la variable msf_annualizedquotachange__c es 0. Lo que supone un 0.0%
Out[221]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c']
In [222]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[222]:
# Tot % Tot
48.00 207919 31.622902
0.00 118697 18.052913
24.00 46376 7.053438
72.00 45925 6.984844
60.00 43108 6.556400
120.00 28119 4.276687
36.00 27732 4.217827
84.00 22833 3.472726
50.00 10626 1.616134
30.00 7533 1.145712
144.00 7394 1.124571
40.00 5521 0.839702
25.00 5257 0.799550
45.00 4661 0.708903
20.00 3932 0.598027
108.00 3853 0.586012
28.00 3495 0.531563
15.00 3448 0.524415
10.00 3315 0.504186
64.00 3000 0.456277
96.00 2767 0.420840
35.88 2766 0.420688
35.00 2551 0.387988
12.00 2207 0.335668
100.00 1840 0.279850
70.00 1759 0.267531
8.00 1751 0.266314
7.00 1666 0.253386
5.00 1659 0.252321
20.04 1625 0.247150
52.00 1619 0.246238
90.00 1468 0.223272
56.00 1443 0.219469
240.00 1280 0.194678
6.00 1260 0.191636
2.00 1217 0.185096
29.90 1182 0.179773
18.00 1048 0.159393
47.80 989 0.150419
80.00 966 0.146921
55.00 953 0.144944
14.95 857 0.130343
132.00 853 0.129735
16.00 850 0.129279
88.00 755 0.114830
44.00 714 0.108594
47.76 658 0.100077
180.00 613 0.093233
65.00 613 0.093233
168.00 610 0.092776
32.00 592 0.090039
76.00 574 0.087301
119.40 528 0.080305
14.00 505 0.076807
17.00 503 0.076502
22.00 490 0.074525
59.64 451 0.068594
33.00 448 0.068137
42.00 410 0.062358
54.00 395 0.060077
44.85 388 0.059012
140.00 371 0.056426
192.00 351 0.053384
71.60 334 0.050799
27.00 325 0.049430
156.00 302 0.045932
4.00 301 0.045780
200.00 299 0.045476
34.00 267 0.040609
160.00 263 0.040000
32.88 231 0.035133
8.97 199 0.030266
11.00 198 0.030114
41.00 189 0.028745
21.00 188 0.028593
68.00 188 0.028593
360.00 181 0.027529
9.00 169 0.025704
44.80 167 0.025399
110.00 143 0.021749
23.92 141 0.021445
130.00 136 0.020685
59.75 133 0.020228
31.00 129 0.019620
300.00 126 0.019164
142.80 124 0.018859
55.76 109 0.016578
480.00 107 0.016274
26.00 106 0.016122
58.00 96 0.014601
3.00 96 0.014601
51.96 94 0.014297
19.00 89 0.013536
62.00 86 0.013080
47.88 85 0.012928
38.00 82 0.012472
17.94 79 0.012015
99.40 79 0.012015
17.15 72 0.010951
40.08 70 0.010646
11.96 66 0.010038
75.00 64 0.009734
104.00 59 0.008973
46.00 59 0.008973
49.70 58 0.008821
52.60 55 0.008365
47.84 54 0.008213
105.00 48 0.007300
89.50 46 0.006996
83.52 44 0.006692
53.00 44 0.006692
400.00 40 0.006084
13.00 40 0.006084
66.00 39 0.005932
46.85 36 0.005475
47.00 35 0.005323
37.00 35 0.005323
600.00 33 0.005019
35.76 33 0.005019
5.98 32 0.004867
95.00 31 0.004715
32.04 30 0.004563
43.00 29 0.004411
720.00 28 0.004259
49.00 26 0.003954
2.99 25 0.003802
51.00 24 0.003650
150.00 24 0.003650
35.88 22 0.003346
119.00 22 0.003346
15.96 22 0.003346
66.96 21 0.003194
63.72 20 0.003042
125.00 20 0.003042
178.20 20 0.003042
119.28 20 0.003042
55.68 19 0.002890
78.00 19 0.002890
320.00 18 0.002738
1200.00 17 0.002586
27.92 17 0.002586
139.20 17 0.002586
92.00 16 0.002433
118.99 16 0.002433
85.00 15 0.002281
112.00 15 0.002281
63.64 14 0.002129
121.80 14 0.002129
51.72 14 0.002129
228.00 13 0.001977
26.91 12 0.001825
40.86 12 0.001825
59.00 12 0.001825
107.04 11 0.001673
280.00 11 0.001673
107.40 11 0.001673
14.16 11 0.001673
118.56 10 0.001521
57.00 10 0.001521
166.56 10 0.001521
69.60 9 0.001369
36.87 9 0.001369
51.82 9 0.001369
29.00 8 0.001217
124.00 8 0.001217
56.64 8 0.001217
46.56 8 0.001217
20.93 8 0.001217
39.00 8 0.001217
71.64 7 0.001065
23.00 7 0.001065
237.60 7 0.001065
95.16 7 0.001065
119.40 6 0.000913
28.31 6 0.000913
39.88 6 0.000913
216.00 6 0.000913
29.76 6 0.000913
45.60 6 0.000913
420.00 6 0.000913
204.00 6 0.000913
115.00 5 0.000760
276.00 5 0.000760
960.00 5 0.000760
47.52 5 0.000760
116.00 5 0.000760
357.00 5 0.000760
59.64 5 0.000760
51.80 5 0.000760
1440.00 5 0.000760
126.00 5 0.000760
89.49 5 0.000760
94.00 5 0.000760
26.32 5 0.000760
220.00 4 0.000608
238.00 4 0.000608
97.92 4 0.000608
8.97 4 0.000608
19.95 4 0.000608
74.00 4 0.000608
135.00 4 0.000608
51.84 4 0.000608
6.58 4 0.000608
79.50 4 0.000608
47.64 4 0.000608
1000.00 4 0.000608
114.00 4 0.000608
63.00 4 0.000608
21.93 4 0.000608
47.76 4 0.000608
59.65 4 0.000608
44.64 4 0.000608
41.88 4 0.000608
67.00 4 0.000608
68.04 3 0.000456
714.00 3 0.000456
21.60 3 0.000456
16.80 3 0.000456
14.88 3 0.000456
260.00 3 0.000456
2400.00 3 0.000456
34.90 3 0.000456
106.00 3 0.000456
33.89 3 0.000456
128.00 3 0.000456
800.00 3 0.000456
82.00 3 0.000456
136.00 3 0.000456
25.04 3 0.000456
49.85 3 0.000456
129.25 3 0.000456
16.95 3 0.000456
59.76 3 0.000456
148.00 3 0.000456
55.92 2 0.000304
53.82 2 0.000304
145.00 2 0.000304
3.99 2 0.000304
780.00 2 0.000304
31.90 2 0.000304
17.34 2 0.000304
3.34 2 0.000304
32.88 2 0.000304
60.60 2 0.000304
38.76 2 0.000304
162.00 2 0.000304
75.60 2 0.000304
118.80 2 0.000304
13.96 2 0.000304
500.00 2 0.000304
356.40 2 0.000304
6.68 2 0.000304
83.00 2 0.000304
64.08 2 0.000304
14400.00 2 0.000304
288.00 2 0.000304
20.95 2 0.000304
43.88 2 0.000304
44.04 2 0.000304
74.04 2 0.000304
102.00 2 0.000304
33.48 2 0.000304
324.00 2 0.000304
44.90 2 0.000304
24.12 2 0.000304
540.00 2 0.000304
122.40 1 0.000152
35.76 1 0.000152
28.80 1 0.000152
264.00 1 0.000152
100.08 1 0.000152
13.60 1 0.000152
115.20 1 0.000152
83.60 1 0.000152
840.00 1 0.000152
154.00 1 0.000152
5.50 1 0.000152
63.60 1 0.000152
141.12 1 0.000152
384.00 1 0.000152
580.00 1 0.000152
86.00 1 0.000152
33.90 1 0.000152
-720.00 1 0.000152
59.28 1 0.000152
-96.00 1 0.000152
143.76 1 0.000152
39.60 1 0.000152
43.84 1 0.000152
29.85 1 0.000152
97.00 1 0.000152
4.50 1 0.000152
202.20 1 0.000152
41.16 1 0.000152
440.00 1 0.000152
87.00 1 0.000152
34.99 1 0.000152
14.50 1 0.000152
475.20 1 0.000152
6.98 1 0.000152
81.52 1 0.000152
71.76 1 0.000152
51.96 1 0.000152
55.68 1 0.000152
44.25 1 0.000152
60.48 1 0.000152
51.77 1 0.000152
59.70 1 0.000152
29.95 1 0.000152
65.76 1 0.000152
3600.00 1 0.000152
81.12 1 0.000152
138.00 1 0.000152
51.85 1 0.000152
190.80 1 0.000152
107.40 1 0.000152
237.96 1 0.000152
560.00 1 0.000152
277.60 1 0.000152
8.66 1 0.000152
49.85 1 0.000152
1920.00 1 0.000152
49.90 1 0.000152
80.04 1 0.000152
296.97 1 0.000152
52.64 1 0.000152
135.44 1 0.000152
64.65 1 0.000152
27.88 1 0.000152
60.04 1 0.000152
41.86 1 0.000152
28.68 1 0.000152
17.95 1 0.000152
-192.00 1 0.000152
109.25 1 0.000152
52.78 1 0.000152
64.32 1 0.000152
900.00 1 0.000152
59.85 1 0.000152
-168.00 1 0.000152
43.86 1 0.000152
29.99 1 0.000152
713.88 1 0.000152
32.10 1 0.000152
59.80 1 0.000152
127.28 1 0.000152
131.88 1 0.000152
83.49 1 0.000152
57.36 1 0.000152
48.85 1 0.000152
47.83 1 0.000152
44.85 1 0.000152
179.00 1 0.000152
28.98 1 0.000152
24000.00 1 0.000152
139.00 1 0.000152
91.92 1 0.000152
178.80 1 0.000152
348.00 1 0.000152
16.08 1 0.000152
297.47 1 0.000152
166.68 1 0.000152
55.80 1 0.000152
77.00 1 0.000152
32.28 1 0.000152
67.76 1 0.000152
17.94 1 0.000152
-108.00 1 0.000152
28.08 1 0.000152
52.08 1 0.000152
33.04 1 0.000152
32.14 1 0.000152
165.12 1 0.000152
432.00 1 0.000152
250.00 1 0.000152
15.92 1 0.000152
98.56 1 0.000152
129.00 1 0.000152
63.24 1 0.000152
71.88 1 0.000152
50.68 1 0.000152
61.68 1 0.000152
103.44 1 0.000152
119.20 1 0.000152
5.60 1 0.000152
297.60 1 0.000152
122.00 1 0.000152
87.52 1 0.000152
20.40 1 0.000152
143.40 1 0.000152
158.60 1 0.000152
236.00 1 0.000152
81.00 1 0.000152
52.88 1 0.000152
16.44 1 0.000152
56.04 1 0.000152
52.68 1 0.000152
35.40 1 0.000152
24.60 1 0.000152
257.57 1 0.000152
660.00 1 0.000152
52.80 1 0.000152
1600.00 1 0.000152
372.00 1 0.000152
69.00 1 0.000152
640.00 1 0.000152
53.64 1 0.000152
44.40 1 0.000152
67.64 1 0.000152
38.28 1 0.000152
350.00 1 0.000152
43.08 1 0.000152
10.50 1 0.000152
700.00 1 0.000152
37.90 1 0.000152
19.80 1 0.000152
63.76 1 0.000152
61.00 1 0.000152
63.80 1 0.000152
109.92 1 0.000152
15.95 1 0.000152
127.28 1 0.000152
147.72 1 0.000152
19.94 1 0.000152
64.75 1 0.000152
133.36 1 0.000152
1320.00 1 0.000152
66.84 1 0.000152
40.68 1 0.000152
197.98 1 0.000152
89.00 1 0.000152
msf_annualizedquotachange__c: incremento de cuota anualizado que se le pediria.
Se puede observar que tiene un 63% de registros vacios.
Analsis de distribución por variables
-> msf_relationshiplevel__c: Variable categorica
In [223]:
# Vamos a realizar analisis por cada variable
var = "msf_relationshiplevel__c"
In [224]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_relationshiplevel__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_relationshiplevel__c es 561. Lo que supone un 0.03110757954751503%
In [225]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[225]:
# Tot % Tot
a0l0O00000k727RQAQ 1742956 96.647313
a0l0O00000k727QQAQ 31607 1.752615
a0l0O00000k727SQAQ 20157 1.117710
a0l0O00000k727TQAQ 6879 0.381442
a0l0O00000k727UQAQ 1259 0.069812
561 0.031108
msf_relationshiplevel__c: tipo de relacion que se desea con el contacto.
Se puede observar que casi no hay vacios.
Analsis de distribución por variables
-> msf_ltvcont__c: Variable numerica
In [226]:
# Vamos a realizar analisis por cada variable
var = "msf_ltvcont__c"
In [227]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_ltvcont__c es 507098. Lo que supone un 28.11870120033115%
El nº de vacios para la variable msf_ltvcont__c es 0. Lo que supone un 0.0%
Out[227]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c']
In [228]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[228]:
# Tot % Tot
1.0 47198 3.640919
10.0 29491 2.274977
30.0 26316 2.030053
20.0 24711 1.906241
60.0 22607 1.743935
... ... ...
3076.1 1 0.000077
3660.1 1 0.000077
7904.0 1 0.000077
5073.1 1 0.000077
1628.7 1 0.000077

100092 rows × 2 columns

msf_ltvcont__c: valor de todas las aportaciones.
Se puede observar que 28% de nulos.
Analsis de distribución por variables
-> mailingstate: Variable categorica
In [229]:
# Vamos a realizar analisis por cada variable
var = "mailingstate"
In [230]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable mailingstate es 0. Lo que supone un 0.0%
El nº de vacios para la variable mailingstate es 499399. Lo que supone un 27.691789872458923%
Out[230]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate']
In [231]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[231]:
# Tot % Tot
499399 27.691790
MADRID 227208 12.598736
BARCELONA 178733 9.910786
VALENCIA/VALÈNCIA 65690 3.642526
BIZKAIA 47719 2.646030
... ... ...
MAZOWIECKIE 1 0.000055
Castilla y la Mancha 1 0.000055
Wisconsin 1 0.000055
San Sebastián 1 0.000055
AvilA 1 0.000055

1205 rows × 2 columns

mailingstate: provincia.
Se puede observar que hay un 5% de vacios. además de muchos registros para las provincias existentes, si se plantea su usoo habrúa que hacer tratamiento de datos intenso.
Analsis de distribución por variables
-> npsp__largest_soft_credit_amount__c: Variable numerica
In [232]:
# Vamos a realizar analisis por cada variable
var = "npsp__largest_soft_credit_amount__c"
In [233]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__largest_soft_credit_amount__c es 1803419. Lo que supone un 100.0%
El nº de vacios para la variable npsp__largest_soft_credit_amount__c es 0. Lo que supone un 0.0%
Out[233]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c']
In [234]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[234]:
# Tot % Tot
npsp__largest_soft_credit_amount__c: mayor importe de operaciones indirectas.
Se puede observar que todos los registros con nulos.
Analsis de distribución por variables
-> npo02__soft_credit_last_year__c: Variable numerica
In [235]:
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_last_year__c"
In [236]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__soft_credit_last_year__c es 1803419. Lo que supone un 100.0%
El nº de vacios para la variable npo02__soft_credit_last_year__c es 0. Lo que supone un 0.0%
Out[236]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c']
In [237]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[237]:
# Tot % Tot
npo02__soft_credit_last_year__c: operaciones indirectas el año pasado.
Se puede observar que todos los registros con nulos.
Analsis de distribución por variables
-> npo02__soft_credit_this_year__c: Variable numerica
In [238]:
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_this_year__c"
In [239]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__soft_credit_this_year__c es 1803419. Lo que supone un 100.0%
El nº de vacios para la variable npo02__soft_credit_this_year__c es 0. Lo que supone un 0.0%
Out[239]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c']
In [240]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[240]:
# Tot % Tot
npo02__soft_credit_this_year__c: operaciones indirectas este año.
Se puede observar que todos los registros con nulos.
Analsis de distribución por variables
-> npo02__soft_credit_two_years_ago__c: Variable numerica
In [241]:
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_two_years_ago__c"
In [242]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__soft_credit_two_years_ago__c es 1803419. Lo que supone un 100.0%
El nº de vacios para la variable npo02__soft_credit_two_years_ago__c es 0. Lo que supone un 0.0%
Out[242]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c']
In [243]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[243]:
# Tot % Tot
npo02__soft_credit_two_years_ago__c: operaciones indirectas hace 2 años.
Se puede observar que todos los registros con nulos.
Analsis de distribución por variables
-> msf_nocaptacionfondoscp__c: Variable booleana
In [244]:
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondoscp__c"
In [245]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondoscp__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_nocaptacionfondoscp__c es 0. Lo que supone un 0.0%
In [246]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[246]:
# Tot % Tot
False 1449578 80.379435
True 353841 19.620565
msf_nocaptacionfondoscp__c: permiso de comuncacion por correo postal.
Se puede observar que no hay vacios.
Analsis de distribución por variables
-> msf_nocaptacionfondosemail__c: Variable booleana
In [247]:
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondosemail__c"
In [248]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondosemail__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_nocaptacionfondosemail__c es 0. Lo que supone un 0.0%
In [249]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[249]:
# Tot % Tot
False 1471944 81.619635
True 331475 18.380365
msf_nocaptacionfondosemail__c: permiso de comuncacion por email.
Se puede observar que no hay vacios.
Analsis de distribución por variables
-> msf_nocaptacionfondosmi__c: Variable booleana
In [250]:
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondosmi__c"
In [251]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondosmi__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_nocaptacionfondosmi__c es 0. Lo que supone un 0.0%
In [252]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[252]:
# Tot % Tot
False 1518698 84.212155
True 284721 15.787845
msf_nocaptacionfondosmi__c: permiso de comuncacion por mi.
Se puede observar que no hay vacios.
Analsis de distribución por variables
-> msf_nocaptacionfondossms__c: Variable booleana
In [253]:
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondossms__c"
In [254]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondossms__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_nocaptacionfondossms__c es 0. Lo que supone un 0.0%
In [255]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[255]:
# Tot % Tot
False 1516147 84.070701
True 287272 15.929299
msf_nocaptacionfondossms__c: permiso de comuncacion por sms.
Se puede observar que no hay vacios.
Analsis de distribución por variables
-> msf_firstcampaignentryrecurringdonor__c: Variable categorica
In [256]:
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaignentryrecurringdonor__c"
In [257]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstcampaignentryrecurringdonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_firstcampaignentryrecurringdonor__c es 809942. Lo que supone un 44.9114709338207%
Out[257]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c']
In [258]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[258]:
# Tot % Tot
809942 44.911471
7013Y000001mr4CQAQ 37787 2.095298
7013Y000001mr2DQAQ 31300 1.735592
7013Y000001mr2cQAA 26419 1.464940
7013Y000001mrCzQAI 25970 1.440042
... ... ...
7013Y000001mrOuQAI 1 0.000055
7013Y000001mrGjQAI 1 0.000055
7013Y000001mrUjQAI 1 0.000055
7013Y000001mqxTQAQ 1 0.000055
7013Y000001mre3QAA 1 0.000055

2565 rows × 2 columns

msf_firstcampaignentryrecurringdonor__c: primera campaña de colaboracion como socio recurrente.
Se puede observar que tiene un 44% de registros a vacio.
Analsis de distribución por variables
-> msf_firstcampaingcolaboration__c: Variable categorica
In [259]:
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaingcolaboration__c"
In [260]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstcampaingcolaboration__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_firstcampaingcolaboration__c es 511991. Lo que supone un 28.390019180234876%
Out[260]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c']
In [261]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[261]:
# Tot % Tot
511991 28.390019
7013Y000001mrCzQAI 164897 9.143577
7013Y000001vYkXQAU 44405 2.462268
7013Y000001mr4CQAQ 34956 1.938318
7013Y000001mrBSQAY 33346 1.849043
... ... ...
7013Y000001mr26QAA 1 0.000055
7013Y000001mr5dQAA 1 0.000055
7013Y000001mr2dQAA 1 0.000055
7013Y000001mrE4QAI 1 0.000055
7013Y000001mrRkQAI 1 0.000055

3747 rows × 2 columns

msf_firstcampaingcolaboration__c: primera campaña de colaboracion economica.
Se puede observar que hay un 28% de vacios.
Analsis de distribución por variables
-> msf_firstannualizedquota__c: Variable numerica
In [262]:
# Vamos a realizar analisis por cada variable
var = "msf_firstannualizedquota__c"
In [263]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstannualizedquota__c es 841860. Lo que supone un 46.68133140440464%
El nº de vacios para la variable msf_firstannualizedquota__c es 0. Lo que supone un 0.0%
Out[263]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c']
In [264]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[264]:
# Tot % Tot
1.200000e+02 287648 29.914753
6.000000e+01 123651 12.859429
1.800000e+02 103083 10.720403
2.400000e+02 51256 5.330510
7.200000e+01 49342 5.131458
1.440000e+02 42521 4.422090
7.212000e+01 32909 3.422463
3.600000e+01 25308 2.631976
3.600000e+02 18085 1.880800
9.600000e+01 16441 1.709827
3.000000e+02 14049 1.461065
1.000000e+02 13112 1.363619
5.000000e+01 11678 1.214486
5.196000e+01 11000 1.143976
0.000000e+00 9343 0.971651
4.000000e+01 8834 0.918716
6.010000e+01 8598 0.894173
8.400000e+01 7852 0.816591
3.005000e+01 7602 0.790591
2.000000e+01 7382 0.767712
8.000000e+01 7025 0.730584
3.000000e+01 6974 0.725281
1.202000e+02 6514 0.677442
1.442400e+02 5434 0.565124
4.800000e+01 5204 0.541204
2.163600e+02 4876 0.507093
2.000000e+02 4820 0.501269
6.000000e+02 4229 0.439807
3.606000e+02 3825 0.397792
1.200000e+01 3772 0.392280
1.000000e+01 3465 0.360352
1.500000e+02 3268 0.339865
1.803000e+01 3166 0.329257
1.320000e+02 2999 0.311889
2.160000e+02 2293 0.238467
1.500000e+01 2293 0.238467
7.200000e+02 1972 0.205084
9.015000e+01 1774 0.184492
2.404000e+02 1725 0.179396
2.500000e+01 1672 0.173884
1.080000e+02 1569 0.163173
9.000000e+01 1565 0.162757
4.800000e+02 1354 0.140813
4.808000e+01 1208 0.125629
2.400000e+01 1120 0.116478
1.200000e+03 1049 0.109094
2.404000e+01 1043 0.108470
3.486000e+01 1041 0.108262
1.600000e+02 939 0.097654
1.560000e+02 856 0.089022
2.040000e+02 850 0.088398
4.000000e+02 814 0.084654
1.502500e+02 781 0.081222
7.212000e+02 779 0.081014
1.394400e+02 744 0.077374
3.606000e+01 724 0.075294
3.612000e+01 713 0.074150
1.082400e+02 652 0.067807
1.920000e+02 627 0.065207
1.040400e+02 598 0.062191
7.000000e+01 534 0.055535
1.803600e+02 503 0.052311
7.500000e+01 439 0.045655
6.010000e+00 382 0.039727
2.500000e+02 377 0.039207
1.730400e+02 376 0.039103
1.680000e+02 376 0.039103
4.200000e+02 353 0.036711
9.316000e+01 344 0.035775
1.202000e+01 341 0.035463
5.000000e+02 306 0.031823
2.884800e+02 304 0.031615
1.039200e+02 287 0.029847
5.000000e+00 273 0.028391
2.520000e+02 270 0.028079
9.616000e+01 262 0.027247
3.608000e+01 260 0.027039
7.224000e+01 254 0.026415
2.640000e+02 248 0.025791
1.803000e+02 248 0.025791
5.768000e+01 243 0.025271
2.880000e+02 243 0.025271
1.400000e+02 240 0.024959
5.200000e+01 229 0.023815
3.005100e+02 204 0.021216
4.183200e+02 204 0.021216
1.800000e+01 193 0.020072
1.000000e+03 177 0.018408
3.000000e+00 176 0.018304
6.000000e+00 146 0.015184
3.500000e+01 141 0.014664
6.012000e+01 141 0.014664
5.400000e+02 140 0.014560
2.885000e+01 138 0.014352
1.800000e+03 138 0.014352
4.207000e+01 136 0.014144
3.200000e+01 133 0.013832
4.320000e+02 116 0.012064
1.154000e+02 114 0.011856
1.250000e+02 112 0.011648
3.462000e+02 108 0.011232
1.442000e+01 102 0.010608
8.414000e+01 100 0.010400
1.080000e+03 99 0.010296
4.500000e+01 95 0.009880
1.923200e+02 89 0.009256
1.081800e+03 86 0.008944
5.770000e+01 86 0.008944
5.409000e+01 80 0.008320
2.400000e+03 80 0.008320
4.327200e+02 77 0.008008
8.000000e+02 75 0.007800
4.200000e+01 75 0.007800
6.010000e+02 71 0.007384
9.600000e+02 70 0.007280
6.010100e+02 68 0.007072
1.300000e+02 66 0.006864
9.000000e+02 64 0.006656
8.400000e+02 62 0.006448
3.960000e+02 61 0.006344
4.808000e+02 61 0.006344
2.760000e+02 60 0.006240
1.440000e+03 60 0.006240
8.000000e+00 60 0.006240
3.120000e+02 59 0.006136
1.500000e+03 55 0.005720
5.500000e+01 54 0.005616
1.081800e+02 53 0.005512
3.726400e+02 52 0.005408
5.769600e+02 48 0.004992
1.100000e+02 47 0.004888
1.204000e+01 46 0.004784
3.600000e+03 46 0.004784
1.803200e+02 45 0.004680
1.682800e+02 45 0.004680
2.404100e+02 43 0.004472
3.614400e+02 41 0.004264
3.240000e+02 41 0.004264
6.500000e+01 40 0.004160
3.200000e+02 39 0.004056
5.048400e+02 39 0.004056
1.442400e+03 39 0.004056
2.524800e+02 39 0.004056
5.400000e+01 38 0.003952
2.280000e+02 38 0.003952
3.840000e+02 37 0.003848
1.600000e+01 36 0.003744
8.654400e+02 36 0.003744
2.800000e+02 36 0.003744
1.082000e+02 36 0.003744
8.460000e+01 36 0.003744
2.000000e+03 33 0.003432
9.020000e+00 33 0.003432
2.200000e+02 32 0.003328
2.800000e+01 32 0.003328
3.650000e+02 30 0.003120
1.204800e+02 30 0.003120
3.360000e+02 30 0.003120
3.000000e+03 30 0.003120
3.500000e+02 29 0.003016
1.040000e+02 29 0.003016
1.094400e+02 27 0.002808
6.924000e+02 26 0.002704
6.000000e+03 26 0.002704
8.416000e+01 25 0.002600
3.720000e+02 25 0.002600
3.606100e+02 24 0.002496
7.813000e+01 24 0.002496
8.800000e+01 24 0.002496
5.040000e+02 23 0.002392
1.700000e+02 23 0.002392
2.600000e+02 23 0.002392
1.803000e+03 23 0.002392
6.024000e+01 22 0.002288
6.600000e+01 22 0.002288
5.600000e+01 22 0.002288
1.503000e+01 22 0.002288
3.900000e+01 21 0.002184
3.010000e+00 20 0.002080
4.680000e+02 20 0.002080
9.200000e+01 18 0.001872
2.160000e+01 18 0.001872
1.750000e+02 18 0.001872
3.800000e+01 18 0.001872
8.652000e+01 17 0.001768
1.824000e+02 17 0.001768
6.600000e+02 17 0.001768
8.500000e+01 16 0.001664
2.308000e+02 16 0.001664
2.103500e+02 16 0.001664
7.800000e+01 16 0.001664
4.400000e+01 16 0.001664
4.080000e+02 15 0.001560
1.520000e+02 15 0.001560
6.800000e+01 14 0.001456
8.640000e+02 14 0.001456
6.400000e+01 13 0.001352
3.012000e+01 13 0.001352
3.005000e+02 13 0.001352
1.200000e+04 13 0.001352
1.400000e+01 13 0.001352
6.120000e+02 13 0.001352
2.200000e+01 13 0.001352
1.201200e+02 12 0.001248
6.240000e+02 12 0.001248
7.000000e+00 12 0.001248
1.020000e+02 12 0.001248
4.000000e+00 12 0.001248
4.500000e+02 12 0.001248
1.719600e+02 12 0.001248
9.036000e+01 11 0.001144
3.606120e+03 11 0.001144
1.480000e+02 11 0.001144
5.760000e+02 11 0.001144
7.200000e+00 11 0.001144
1.202040e+03 11 0.001144
7.600000e+01 10 0.001040
4.332000e+01 10 0.001040
1.120000e+02 10 0.001040
7.920000e+02 10 0.001040
7.210000e+00 10 0.001040
9.012000e+01 9 0.000936
1.450000e+02 9 0.000936
4.508000e+01 9 0.000936
2.600000e+01 9 0.000936
1.280000e+02 9 0.000936
3.005200e+02 9 0.000936
3.480000e+02 9 0.000936
2.160000e+03 9 0.000936
9.000000e+00 8 0.000832
7.400000e+01 8 0.000832
5.289000e+01 8 0.000832
3.400000e+01 8 0.000832
7.228800e+02 8 0.000832
2.100000e+02 8 0.000832
7.800000e+02 7 0.000728
5.772000e+01 7 0.000728
6.200000e+01 7 0.000728
6.490800e+02 7 0.000728
7.300000e+01 7 0.000728
1.300000e+01 7 0.000728
5.300000e+01 7 0.000728
4.560000e+02 7 0.000728
3.365600e+02 7 0.000728
1.050000e+02 7 0.000728
6.611000e+01 7 0.000728
1.117920e+03 7 0.000728
1.350000e+02 7 0.000728
9.016000e+01 6 0.000624
1.160000e+02 6 0.000624
4.800000e+03 6 0.000624
1.020000e+03 6 0.000624
1.444000e+01 6 0.000624
7.932000e+01 6 0.000624
7.000000e+02 6 0.000624
5.052000e+01 6 0.000624
5.200000e+02 6 0.000624
2.250000e+02 6 0.000624
1.600000e+03 6 0.000624
2.163600e+03 6 0.000624
2.880000e+01 6 0.000624
7.212200e+02 6 0.000624
1.000000e+00 6 0.000624
3.700000e+01 5 0.000520
9.360000e+02 5 0.000520
9.996000e+01 5 0.000520
2.300000e+02 5 0.000520
1.100000e+01 5 0.000520
1.920000e+03 5 0.000520
1.240000e+02 5 0.000520
7.200000e-01 5 0.000520
1.320000e+03 5 0.000520
9.900000e+01 5 0.000520
5.409600e+02 5 0.000520
2.164000e+01 5 0.000520
1.440000e+01 5 0.000520
9.015200e+02 5 0.000520
7.200000e+03 5 0.000520
2.115600e+02 5 0.000520
4.400000e+02 4 0.000416
9.375600e+02 4 0.000416
1.560000e+03 4 0.000416
5.000000e+03 4 0.000416
1.360000e+02 4 0.000416
1.620000e+02 4 0.000416
9.496000e+01 4 0.000416
5.592000e+01 4 0.000416
4.507600e+02 4 0.000416
3.300000e+01 4 0.000416
1.700000e+01 4 0.000416
2.700000e+01 4 0.000416
5.412000e+01 4 0.000416
1.260000e+02 4 0.000416
6.400000e+02 4 0.000416
1.081200e+02 4 0.000416
1.400000e+03 4 0.000416
8.660000e+00 4 0.000416
1.250000e+01 4 0.000416
2.100000e+01 4 0.000416
2.404040e+03 4 0.000416
1.650000e+02 4 0.000416
2.409600e+02 4 0.000416
7.560000e+02 4 0.000416
1.502400e+02 4 0.000416
2.700000e+02 4 0.000416
2.300000e+01 4 0.000416
4.330000e+00 4 0.000416
4.000000e+03 3 0.000312
3.100000e+01 3 0.000312
4.207100e+02 3 0.000312
4.328000e+01 3 0.000312
1.081840e+03 3 0.000312
2.705000e+01 3 0.000312
9.616400e+02 3 0.000312
5.202000e+01 3 0.000312
7.500000e+02 3 0.000312
2.040000e+03 3 0.000312
1.226400e+02 3 0.000312
3.900000e+03 3 0.000312
2.104000e+01 3 0.000312
1.129900e+02 3 0.000312
7.513000e+01 3 0.000312
2.884920e+03 3 0.000312
1.009680e+03 3 0.000312
3.004000e+01 3 0.000312
3.330000e+02 3 0.000312
6.972000e+01 3 0.000312
3.996000e+01 3 0.000312
1.804000e+01 3 0.000312
1.803040e+03 3 0.000312
3.846400e+02 3 0.000312
2.884000e+01 3 0.000312
2.750000e+02 3 0.000312
4.440000e+02 3 0.000312
1.732000e+01 3 0.000312
1.212000e+03 3 0.000312
1.510000e+02 3 0.000312
3.125200e+02 3 0.000312
2.004000e+03 3 0.000312
3.400000e+02 3 0.000312
1.900000e+02 3 0.000312
6.360000e+02 3 0.000312
9.372000e+01 3 0.000312
5.100000e+01 3 0.000312
1.983300e+02 3 0.000312
6.480000e+02 3 0.000312
1.532600e+02 3 0.000312
1.983600e+02 3 0.000312
4.600000e+02 2 0.000208
1.800000e+04 2 0.000208
3.750000e+02 2 0.000208
5.988000e+01 2 0.000208
3.660000e+02 2 0.000208
1.280200e+02 2 0.000208
7.356000e+02 2 0.000208
3.666000e+01 2 0.000208
7.933200e+02 2 0.000208
2.884900e+02 2 0.000208
1.830000e+02 2 0.000208
1.850000e+02 2 0.000208
1.210000e+02 2 0.000208
4.800000e+00 2 0.000208
2.480000e+02 2 0.000208
4.600000e+01 2 0.000208
8.414000e+02 2 0.000208
1.322400e+02 2 0.000208
4.327000e+01 2 0.000208
1.200100e+02 2 0.000208
6.100000e+01 2 0.000208
2.644400e+02 2 0.000208
6.492000e+01 2 0.000208
1.640000e+02 2 0.000208
4.920000e+02 2 0.000208
5.500000e+02 2 0.000208
3.250000e+02 2 0.000208
2.520000e+01 2 0.000208
8.200000e+01 2 0.000208
1.502600e+02 2 0.000208
2.406000e+02 2 0.000208
7.440000e+01 2 0.000208
1.010000e+02 2 0.000208
6.500000e+02 2 0.000208
2.019600e+02 2 0.000208
2.403600e+02 2 0.000208
3.602400e+02 2 0.000208
4.200000e+03 2 0.000208
1.622400e+02 2 0.000208
8.700000e+01 2 0.000208
7.200000e+04 2 0.000208
3.006000e+01 2 0.000208
3.300000e+02 2 0.000208
1.802800e+02 2 0.000208
2.598000e+01 2 0.000208
9.999600e+02 2 0.000208
2.000000e+00 2 0.000208
7.700000e+01 2 0.000208
9.320000e+00 2 0.000208
2.900000e+01 2 0.000208
7.250000e+02 2 0.000208
1.202020e+03 2 0.000208
1.021700e+02 2 0.000208
4.808100e+02 2 0.000208
1.150000e+02 2 0.000208
9.500000e+01 2 0.000208
1.959600e+02 2 0.000208
5.520000e+02 2 0.000208
3.900000e+02 2 0.000208
1.008000e+03 2 0.000208
1.230000e+02 2 0.000208
1.586400e+02 2 0.000208
7.572000e+01 2 0.000208
1.382300e+02 2 0.000208
1.460000e+02 2 0.000208
7.212120e+03 2 0.000208
1.444800e+02 2 0.000208
4.300000e+01 2 0.000208
6.396000e+01 2 0.000208
4.519600e+02 2 0.000208
6.960000e+02 2 0.000208
1.740000e+02 2 0.000208
5.880000e+02 2 0.000208
8.656000e+01 2 0.000208
7.452000e+01 2 0.000208
4.320000e+03 2 0.000208
1.394000e+02 2 0.000208
7.320000e+01 2 0.000208
2.220000e+02 2 0.000208
8.040000e+02 2 0.000208
6.200000e+02 2 0.000208
7.320000e+02 2 0.000208
1.100000e+03 2 0.000208
7.440000e+02 2 0.000208
1.260000e+03 2 0.000208
3.607200e+02 2 0.000208
1.382000e+01 2 0.000208
3.060000e+02 2 0.000208
1.000100e+02 2 0.000208
1.200000e+00 2 0.000208
2.440000e+02 2 0.000208
1.355880e+03 2 0.000208
1.684000e+01 2 0.000208
1.960000e+02 2 0.000208
1.562800e+02 2 0.000208
9.100000e+01 2 0.000208
2.061500e+02 2 0.000208
5.408000e+01 2 0.000208
7.992000e+01 2 0.000208
1.250000e+03 2 0.000208
2.880000e+03 2 0.000208
5.196000e+02 2 0.000208
1.110000e+02 2 0.000208
6.130800e+02 2 0.000208
8.160000e+02 2 0.000208
1.154400e+02 2 0.000208
3.246000e+02 2 0.000208
2.379600e+02 2 0.000208
1.262100e+02 2 0.000208
2.550000e+02 1 0.000104
8.640000e+01 1 0.000104
9.204000e+01 1 0.000104
5.280000e+02 1 0.000104
9.999000e+01 1 0.000104
5.600000e+02 1 0.000104
4.692000e+01 1 0.000104
5.280000e+01 1 0.000104
6.840000e+02 1 0.000104
9.324000e+01 1 0.000104
5.160000e+01 1 0.000104
6.060000e+01 1 0.000104
2.240000e+02 1 0.000104
6.000000e-01 1 0.000104
6.015000e+01 1 0.000104
9.840000e+03 1 0.000104
2.476800e+02 1 0.000104
3.110000e+02 1 0.000104
6.600000e+03 1 0.000104
6.800000e+02 1 0.000104
2.100000e+03 1 0.000104
2.560000e+02 1 0.000104
1.716000e+02 1 0.000104
9.015100e+02 1 0.000104
3.010000e+02 1 0.000104
1.900000e+01 1 0.000104
2.150000e+02 1 0.000104
5.800000e+01 1 0.000104
1.202400e+02 1 0.000104
5.908000e+01 1 0.000104
1.680000e+03 1 0.000104
1.665600e+02 1 0.000104
1.250400e+02 1 0.000104
1.159200e+02 1 0.000104
6.235200e+02 1 0.000104
1.442440e+03 1 0.000104
2.720000e+02 1 0.000104
2.439600e+02 1 0.000104
3.800000e+02 1 0.000104
1.000800e+02 1 0.000104
5.040000e+01 1 0.000104
3.350000e+02 1 0.000104
2.253800e+03 1 0.000104
3.040000e+01 1 0.000104
1.052400e+02 1 0.000104
1.893600e+02 1 0.000104
1.446000e+02 1 0.000104
5.100000e+02 1 0.000104
1.296000e+03 1 0.000104
5.700000e+01 1 0.000104
2.560000e+01 1 0.000104
3.320000e+02 1 0.000104
1.812000e+02 1 0.000104
3.726000e+01 1 0.000104
2.960000e+02 1 0.000104
1.470000e+02 1 0.000104
1.860000e+03 1 0.000104
5.288000e+01 1 0.000104
1.140000e+03 1 0.000104
6.720000e+01 1 0.000104
6.876000e+01 1 0.000104
9.912000e+01 1 0.000104
1.658400e+02 1 0.000104
2.999000e+01 1 0.000104
1.238000e+02 1 0.000104
1.452000e+02 1 0.000104
1.208000e+02 1 0.000104
2.050000e+02 1 0.000104
2.000400e+02 1 0.000104
6.016000e+01 1 0.000104
4.208000e+01 1 0.000104
2.180000e+02 1 0.000104
4.100000e+01 1 0.000104
1.002000e+03 1 0.000104
7.812000e+01 1 0.000104
3.954800e+02 1 0.000104
3.005060e+04 1 0.000104
2.920000e+02 1 0.000104
1.472500e+02 1 0.000104
1.478520e+03 1 0.000104
6.346800e+02 1 0.000104
4.095600e+02 1 0.000104
2.496000e+03 1 0.000104
4.992000e+01 1 0.000104
6.001000e+01 1 0.000104
5.900000e+01 1 0.000104
1.586640e+03 1 0.000104
4.700000e+01 1 0.000104
1.056000e+02 1 0.000104
1.340000e+02 1 0.000104
8.246000e+02 1 0.000104
1.089600e+02 1 0.000104
1.947600e+02 1 0.000104
2.310000e+02 1 0.000104
6.660000e+01 1 0.000104
4.116000e+01 1 0.000104
1.300000e+03 1 0.000104
3.768000e+02 1 0.000104
2.340000e+02 1 0.000104
1.420000e+02 1 0.000104
2.388000e+02 1 0.000104
2.850000e+02 1 0.000104
3.780000e+02 1 0.000104
9.400000e+01 1 0.000104
1.036800e+02 1 0.000104
3.906600e+02 1 0.000104
4.928400e+02 1 0.000104
1.080000e+01 1 0.000104
5.048000e+01 1 0.000104
9.600000e+00 1 0.000104
1.380000e+03 1 0.000104
9.720000e+01 1 0.000104
9.096000e+01 1 0.000104
1.002000e+02 1 0.000104
1.870000e+02 1 0.000104
1.027200e+02 1 0.000104
9.232000e+01 1 0.000104
1.268400e+02 1 0.000104
3.885000e+01 1 0.000104
1.298400e+02 1 0.000104
5.160000e+02 1 0.000104
3.305600e+02 1 0.000104
3.336000e+02 1 0.000104
1.033760e+03 1 0.000104
2.043600e+02 1 0.000104
1.284000e+03 1 0.000104
5.944800e+02 1 0.000104
4.688400e+02 1 0.000104
1.800000e+00 1 0.000104
7.510000e+00 1 0.000104
6.010200e+02 1 0.000104
9.020000e+01 1 0.000104
3.065200e+02 1 0.000104
4.028000e+01 1 0.000104
8.292000e+01 1 0.000104
2.456676e+07 1 0.000104
2.596800e+02 1 0.000104
1.430400e+02 1 0.000104
2.200000e+03 1 0.000104
6.132000e+01 1 0.000104
1.322200e+02 1 0.000104
2.704600e+02 1 0.000104
7.220000e+01 1 0.000104
1.200000e+05 1 0.000104
1.840000e+02 1 0.000104
1.710000e+02 1 0.000104
1.502530e+03 1 0.000104
1.202000e+03 1 0.000104
4.580000e+02 1 0.000104
5.949600e+02 1 0.000104
2.307600e+02 1 0.000104
5.109000e+01 1 0.000104
3.124000e+01 1 0.000104
3.100000e+02 1 0.000104
6.010120e+03 1 0.000104
1.620000e+03 1 0.000104
2.524400e+02 1 0.000104
1.060000e+02 1 0.000104
6.720000e+02 1 0.000104
9.012000e+02 1 0.000104
2.500000e+03 1 0.000104
9.600000e+03 1 0.000104
4.182000e+02 1 0.000104
1.045760e+03 1 0.000104
9.840000e+02 1 0.000104
1.552000e+01 1 0.000104
2.046000e+02 1 0.000104
1.146720e+03 1 0.000104
1.030000e+02 1 0.000104
1.875600e+02 1 0.000104
9.720000e+02 1 0.000104
9.240000e+00 1 0.000104
3.612000e+03 1 0.000104
2.524300e+02 1 0.000104
2.402000e+01 1 0.000104
1.083600e+02 1 0.000104
1.092000e+02 1 0.000104
6.242400e+02 1 0.000104
5.870000e+00 1 0.000104
2.928000e+02 1 0.000104
1.983200e+02 1 0.000104
2.760000e+03 1 0.000104
8.166000e+03 1 0.000104
2.803600e+02 1 0.000104
1.980000e+02 1 0.000104
1.524000e+03 1 0.000104
1.203000e+01 1 0.000104
8.052648e+09 1 0.000104
1.080000e+06 1 0.000104
1.164000e+06 1 0.000104
7.596000e+01 1 0.000104
2.058000e+04 1 0.000104
1.253400e+02 1 0.000104
5.400000e+03 1 0.000104
1.239600e+02 1 0.000104
1.939200e+02 1 0.000104
9.015240e+03 1 0.000104
2.636000e+01 1 0.000104
7.230000e+01 1 0.000104
1.046400e+02 1 0.000104
6.008000e+01 1 0.000104
1.104000e+02 1 0.000104
4.059600e+02 1 0.000104
2.957040e+03 1 0.000104
7.220000e+00 1 0.000104
1.009200e+02 1 0.000104
4.900000e+01 1 0.000104
2.425000e+02 1 0.000104
4.484000e+01 1 0.000104
5.152800e+02 1 0.000104
8.925000e+01 1 0.000104
2.956800e+02 1 0.000104
2.410000e+02 1 0.000104
7.080000e+02 1 0.000104
2.064000e+03 1 0.000104
1.550000e+02 1 0.000104
1.834800e+02 1 0.000104
2.170800e+02 1 0.000104
2.401200e+02 1 0.000104
9.800000e+01 1 0.000104
3.889200e+02 1 0.000104
2.184000e+03 1 0.000104
4.700000e+02 1 0.000104
1.008000e+04 1 0.000104
1.536000e+03 1 0.000104
1.009600e+02 1 0.000104
5.460000e+01 1 0.000104
1.001500e+02 1 0.000104
1.678800e+02 1 0.000104
6.005000e+01 1 0.000104
1.812000e+03 1 0.000104
1.298160e+03 1 0.000104
1.490400e+02 1 0.000104
3.604000e+01 1 0.000104
3.996000e+02 1 0.000104
7.620000e+01 1 0.000104
3.700000e+02 1 0.000104
9.960000e+01 1 0.000104
8.016000e+01 1 0.000104
1.975200e+02 1 0.000104
6.300000e+01 1 0.000104
2.199720e+03 1 0.000104
1.056000e+03 1 0.000104
1.002800e+02 1 0.000104
1.090000e+02 1 0.000104
1.188000e+02 1 0.000104
6.006000e+01 1 0.000104
1.802400e+02 1 0.000104
1.500000e+04 1 0.000104
1.514400e+02 1 0.000104
6.020000e+02 1 0.000104
1.500100e+02 1 0.000104
3.000100e+02 1 0.000104
2.524200e+03 1 0.000104
2.193600e+02 1 0.000104
2.900000e+02 1 0.000104
1.501500e+02 1 0.000104
2.451600e+02 1 0.000104
5.493000e+01 1 0.000104
8.655000e+01 1 0.000104
1.211640e+03 1 0.000104
2.352000e+03 1 0.000104
2.196000e+02 1 0.000104
1.203600e+02 1 0.000104
3.607000e+01 1 0.000104
8.880000e+02 1 0.000104
1.200800e+02 1 0.000104
8.280000e+02 1 0.000104
7.204000e+01 1 0.000104
1.201200e+04 1 0.000104
8.900000e+01 1 0.000104
3.480000e+03 1 0.000104
3.230000e+02 1 0.000104
1.236000e+03 1 0.000104
4.119600e+02 1 0.000104
1.436400e+02 1 0.000104
1.340000e+01 1 0.000104
2.282400e+03 1 0.000104
5.950000e+01 1 0.000104
4.520000e+02 1 0.000104
6.480000e+01 1 0.000104
1.599600e+02 1 0.000104
1.220000e+02 1 0.000104
msf_firstannualizedquota__c: importe anualizado del primer compromiso como socio.
Se puede observar que existe un 46% de nulos, los importes más comunes van desde 60 a 240€
Analsis de distribución por variables
-> msf_program__c: Variable categorica
In [265]:
# Vamos a realizar analisis por cada variable
var = "msf_program__c"
In [266]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_program__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_program__c es 36959. Lo que supone un 2.049385084664185%
In [267]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[267]:
# Tot % Tot
Reactivación bajas MASS 473018 26.228957
Cultivación socios MASS 432996 24.009728
Conversión prospectos 402715 22.330640
Reactivación/conversión EXDonantes MASS 283100 15.697960
Cultivación/conversión Donantes MASS 61383 3.403702
36959 2.049385
Prospectos Empresas & Colectivos Mass 30148 1.671714
Empresas y Colectivos Mass 26005 1.441983
Retención 1r año MASS 24156 1.339456
Cultivación socios MID 17102 0.948310
Mid+ Donors 5336 0.295882
Instituciones Públicas Mass 2447 0.135687
Otros programas transversales 1539 0.085338
Cultivación/conversión Donantes MID 1461 0.081013
Testamentarios 1170 0.064877
Empresas y Colectivos Mid, Mid + 762 0.042253
Empresas y Colectivos Estratégicas 653 0.036209
Otros 12Few+ 373 0.020683
Reactivación bajas MID 372 0.020627
Fundaciones Mass 293 0.016247
Fundaciones Estratégicas 246 0.013641
Reactivación/conversión EXDonantes MID 216 0.011977
Vehículo donación de Gran Donante = YES 215 0.011922
Retención 1r año MID 188 0.010425
Prospectos Fundaciones Mass 165 0.009149
Públicos Especiales 146 0.008096
Major Donors 115 0.006377
Potenciales a Major Donors 69 0.003826
Otros 121 29 0.001608
Fundaciones Mid, Mid + 21 0.001164
Instituciones Públicas Mid y Mid + 20 0.001109
Instituciones Públicas Estratégicas 1 0.000055
msf_program__c: programa al que pertenece.
Se puede observar que la variable tiene un 3% de vacios dividiendose principalmente en dos.
Analsis de distribución por variables
-> msf_programaherencias__c: Variable booleana
In [268]:
# Vamos a realizar analisis por cada variable
var = "msf_programaherencias__c"
In [269]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_programaherencias__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_programaherencias__c es 0. Lo que supone un 0.0%
In [270]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[270]:
# Tot % Tot
False 1795692 99.571536
True 7727 0.428464
msf_programaherencias__c: indicador de algun tipo de relacion con el programa de herencias.
Se puede observar que toma el valor de falso en casi todos los casos.
Analsis de distribución por variables
-> msf_programais__c: Variable booleana
In [271]:
# Vamos a realizar analisis por cada variable
var = "msf_programais__c"
In [272]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_programais__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_programais__c es 0. Lo que supone un 0.0%
In [273]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[273]:
# Tot % Tot
False 1802909 99.97172
True 510 0.02828
msf_programais__c: indicador de promotor en iniciativa solidaria.
Se puede observar que toma el valor de falso en casi todos los casos.
Analsis de distribución por variables
-> msf_pressurecomplaint__c: Variable booleana
In [274]:
# Vamos a realizar analisis por cada variable
var = "msf_pressurecomplaint__c"
In [275]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_pressurecomplaint__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_pressurecomplaint__c es 0. Lo que supone un 0.0%
In [276]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[276]:
# Tot % Tot
False 1798033 99.701345
True 5386 0.298655
msf_pressurecomplaint__c: queja por presión telemarketing.
Se puede observar que toma el valor de falso en casi todos los casos.
Analsis de distribución por variables
-> msf_recencydonorcont__c: Variable numerica
In [277]:
# Vamos a realizar analisis por cada variable
var = "msf_recencydonorcont__c"
In [278]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_recencydonorcont__c es 1177448. Lo que supone un 65.28976349922009%
El nº de vacios para la variable msf_recencydonorcont__c es 0. Lo que supone un 0.0%
Out[278]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_recencydonorcont__c']
In [279]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[279]:
# Tot % Tot
2244.0 9884 1.578987
218.0 7264 1.160437
1102.0 7023 1.121937
128.0 6653 1.062829
1132.0 4539 0.725113
... ... ...
11426.0 1 0.000160
11840.0 1 0.000160
7209.0 1 0.000160
12103.0 1 0.000160
10520.0 1 0.000160

10781 rows × 2 columns

msf_recencydonorcont__c: numero de dias desde el ultimo donativo.
Se puede observar que al tener muchos de los donantes recurrentes no hacer donanciones puntuales pues tienen el registro a nulo.
Analsis de distribución por variables
-> msf_recencyrecurringdonorcont__c: Variable numerica
In [280]:
# Vamos a realizar analisis por cada variable
var = "msf_recencyrecurringdonorcont__c"
In [281]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_recencyrecurringdonorcont__c es 868629. Lo que supone un 48.165678635968675%
El nº de vacios para la variable msf_recencyrecurringdonorcont__c es 0. Lo que supone un 0.0%
Out[281]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_recencydonorcont__c',
 'msf_recencyrecurringdonorcont__c']
In [282]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[282]:
# Tot % Tot
4.0 391646 41.896683
36.0 20951 2.241252
66.0 20310 2.172680
186.0 13427 1.436365
156.0 13005 1.391222
128.0 10337 1.105810
218.0 10233 1.094684
95.0 8499 0.909188
247.0 7675 0.821040
340.0 7139 0.763701
277.0 6996 0.748403
309.0 6101 0.652660
1983.0 5297 0.566651
2012.0 4219 0.451331
1648.0 3902 0.417420
2042.0 3888 0.415922
1314.0 3803 0.406829
1678.0 3790 0.405439
583.0 3755 0.401694
948.0 3667 0.392281
1955.0 3615 0.386718
1283.0 3555 0.380299
1769.0 3360 0.359439
1251.0 3341 0.357406
550.0 3333 0.356551
1740.0 3308 0.353876
1832.0 3256 0.348314
1922.0 3252 0.347886
914.0 3250 0.347672
1375.0 3210 0.343393
401.0 3201 0.342430
1405.0 3195 0.341788
1223.0 3187 0.340932
1618.0 3179 0.340076
1437.0 3175 0.339648
612.0 3165 0.338579
1802.0 3134 0.335262
858.0 3133 0.335155
2105.0 3127 0.334514
1863.0 3100 0.331625
1009.0 3095 0.331090
644.0 3092 0.330769
1468.0 3088 0.330342
766.0 3082 0.329700
368.0 3053 0.326597
2074.0 3053 0.326597
1709.0 3050 0.326276
886.0 3010 0.321997
1590.0 2989 0.319751
674.0 2986 0.319430
2378.0 2958 0.316435
1040.0 2954 0.316007
2410.0 2924 0.312798
493.0 2910 0.311300
1892.0 2902 0.310444
976.0 2890 0.309160
2136.0 2887 0.308839
1342.0 2887 0.308839
521.0 2851 0.304988
1559.0 2850 0.304881
827.0 2806 0.300174
431.0 2800 0.299533
462.0 2772 0.296537
795.0 2755 0.294719
736.0 2754 0.294612
1102.0 2751 0.294291
1528.0 2680 0.286695
704.0 2673 0.285947
2167.0 2618 0.280063
1496.0 2591 0.277175
2196.0 2528 0.270435
1069.0 2523 0.269900
1192.0 2511 0.268616
1131.0 2437 0.260700
2775.0 2431 0.260058
2469.0 2410 0.257812
2347.0 2408 0.257598
2319.0 2404 0.257170
2258.0 2376 0.254175
2228.0 2346 0.250965
1161.0 2302 0.246259
2287.0 2289 0.244868
2714.0 2283 0.244226
2439.0 2275 0.243370
3869.0 2256 0.241338
2742.0 2228 0.238342
2501.0 2219 0.237380
3109.0 2142 0.229142
3474.0 2139 0.228821
4205.0 2138 0.228714
3140.0 2119 0.226682
2532.0 2103 0.224970
2837.0 2095 0.224115
2563.0 2091 0.223687
3839.0 2055 0.219835
2654.0 2041 0.218338
2685.0 2040 0.218231
3932.0 2028 0.216947
2867.0 2026 0.216733
4175.0 2021 0.216198
2593.0 2016 0.215663
4237.0 2013 0.215342
3078.0 1989 0.212775
3809.0 1982 0.212026
3050.0 1980 0.211812
2804.0 1977 0.211491
3020.0 1970 0.210743
3442.0 1966 0.210315
2623.0 1932 0.206677
3505.0 1912 0.204538
2929.0 1903 0.203575
2958.0 1889 0.202077
3781.0 1834 0.196194
2896.0 1822 0.194910
4114.0 1812 0.193840
4146.0 1811 0.193733
3566.0 1791 0.191594
4083.0 1788 0.191273
3900.0 1780 0.190417
3414.0 1780 0.190417
3201.0 1767 0.189026
3960.0 1761 0.188385
4023.0 1753 0.187529
2987.0 1736 0.185710
3749.0 1710 0.182929
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3230.0 1660 0.177580
3993.0 1634 0.174799
3384.0 1633 0.174692
3351.0 1630 0.174371
3263.0 1628 0.174157
3293.0 1607 0.171910
3169.0 1603 0.171482
3659.0 1585 0.169557
4601.0 1539 0.164636
4569.0 1539 0.164636
4512.0 1533 0.163994
3719.0 1526 0.163245
4051.0 1517 0.162282
4266.0 1482 0.158538
3320.0 1475 0.157789
4295.0 1435 0.153510
4327.0 1429 0.152869
4540.0 1424 0.152334
3533.0 1422 0.152120
4390.0 1389 0.148590
4420.0 1369 0.146450
3627.0 1365 0.146022
4481.0 1352 0.144631
3687.0 1345 0.143883
4450.0 1275 0.136394
5268.0 1271 0.135966
5300.0 1247 0.133399
4358.0 1239 0.132543
4660.0 1216 0.130083
5332.0 1207 0.129120
5392.0 1188 0.127087
4692.0 1180 0.126232
4966.0 1179 0.126125
4631.0 1175 0.125697
5423.0 1155 0.123557
4723.0 1097 0.117353
4755.0 1082 0.115748
4814.0 1067 0.114143
5665.0 1066 0.114036
5605.0 1043 0.111576
4784.0 1041 0.111362
4846.0 1035 0.110720
5240.0 1033 0.110506
5633.0 1029 0.110078
4905.0 1028 0.109971
4877.0 1025 0.109650
5210.0 1024 0.109543
5027.0 1023 0.109436
4933.0 1022 0.109329
5697.0 985 0.105371
4996.0 967 0.103446
5482.0 947 0.101306
5360.0 945 0.101092
5057.0 940 0.100557
5545.0 927 0.099167
5573.0 895 0.095743
5178.0 895 0.095743
5514.0 867 0.092748
5941.0 863 0.092320
5147.0 847 0.090609
5119.0 845 0.090395
5756.0 842 0.090074
5727.0 836 0.089432
5848.0 809 0.086544
5787.0 806 0.086223
6029.0 772 0.082585
5972.0 769 0.082264
5087.0 760 0.081302
5447.0 750 0.080232
5997.0 717 0.076702
5909.0 702 0.075097
5819.0 700 0.074883
5877.0 646 0.069106
7738.0 643 0.068786
6123.0 628 0.067181
6364.0 603 0.064506
6393.0 597 0.063865
6062.0 571 0.061083
6151.0 542 0.057981
6426.0 532 0.056911
6336.0 521 0.055734
6245.0 521 0.055734
6305.0 520 0.055627
6214.0 519 0.055520
6091.0 518 0.055414
6274.0 508 0.054344
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6487.0 448 0.047925
6669.0 443 0.047390
6518.0 439 0.046962
6700.0 428 0.045786
6549.0 423 0.045251
6728.0 418 0.044716
6456.0 407 0.043539
6639.0 396 0.042362
6759.0 390 0.041721
6609.0 385 0.041186
6882.0 380 0.040651
6578.0 358 0.038297
7097.0 355 0.037976
7068.0 329 0.035195
7128.0 323 0.034553
6946.0 320 0.034232
6849.0 315 0.033697
7037.0 307 0.032842
7493.0 286 0.030595
6820.0 286 0.030595
7462.0 277 0.029632
6789.0 273 0.029204
8315.0 267 0.028563
7007.0 263 0.028135
7585.0 254 0.027172
7950.0 250 0.026744
8192.0 242 0.025888
7403.0 236 0.025246
6976.0 236 0.025246
7159.0 235 0.025139
7858.0 232 0.024818
7220.0 215 0.023000
7312.0 207 0.022144
7615.0 207 0.022144
7250.0 207 0.022144
6915.0 205 0.021930
7434.0 203 0.021716
7189.0 200 0.021395
8042.0 200 0.021395
9776.0 194 0.020753
7342.0 187 0.020004
8133.0 187 0.020004
7646.0 186 0.019898
8223.0 185 0.019791
7373.0 183 0.019577
8164.0 178 0.019042
7281.0 176 0.018828
7524.0 171 0.018293
7889.0 171 0.018293
7554.0 169 0.018079
7827.0 165 0.017651
9684.0 162 0.017330
9411.0 162 0.017330
8254.0 157 0.016795
8494.0 156 0.016688
8407.0 154 0.016474
9288.0 154 0.016474
7768.0 153 0.016367
7980.0 152 0.016260
7919.0 151 0.016153
9653.0 148 0.015832
8923.0 146 0.015618
10141.0 146 0.015618
8558.0 145 0.015512
7799.0 145 0.015512
9594.0 143 0.015298
9959.0 143 0.015298
8954.0 136 0.014549
9868.0 133 0.014228
8345.0 131 0.014014
10019.0 130 0.013907
8072.0 130 0.013907
9503.0 129 0.013800
8773.0 129 0.013800
8468.0 124 0.013265
8864.0 123 0.013158
9319.0 122 0.013051
8011.0 121 0.012944
9229.0 120 0.012837
9046.0 120 0.012837
8376.0 118 0.012623
8103.0 117 0.012516
8284.0 114 0.012195
2198.0 110 0.011767
8437.0 109 0.011660
9260.0 108 0.011553
10049.0 107 0.011446
8577.0 107 0.011446
8681.0 107 0.011446
10384.0 107 0.011446
7668.0 104 0.011125
8620.0 101 0.010805
8521.0 100 0.010698
10234.0 98 0.010484
9138.0 97 0.010377
10325.0 91 0.009735
8895.0 90 0.009628
8985.0 89 0.009521
9625.0 85 0.009093
10414.0 84 0.008986
7665.0 83 0.008879
8803.0 82 0.008772
2379.0 77 0.008237
8711.0 75 0.008023
9990.0 73 0.007809
2045.0 72 0.007702
8742.0 70 0.007488
9715.0 68 0.007274
8650.0 68 0.007274
2014.0 67 0.007167
2776.0 67 0.007167
2990.0 66 0.007060
2348.0 66 0.007060
7695.0 66 0.007060
3079.0 65 0.006953
2259.0 65 0.006953
2075.0 64 0.006846
2320.0 63 0.006739
2624.0 63 0.006739
2898.0 62 0.006633
2471.0 61 0.006526
2959.0 59 0.006312
10111.0 59 0.006312
9533.0 59 0.006312
9076.0 58 0.006205
2806.0 57 0.006098
10356.0 57 0.006098
9168.0 55 0.005884
3051.0 55 0.005884
9805.0 55 0.005884
3416.0 54 0.005777
9745.0 54 0.005777
2289.0 53 0.005670
2745.0 53 0.005670
1741.0 52 0.005563
3141.0 52 0.005563
10502.0 52 0.005563
3110.0 52 0.005563
8834.0 52 0.005563
10264.0 52 0.005563
2440.0 51 0.005456
1924.0 50 0.005349
3232.0 50 0.005349
9107.0 50 0.005349
2106.0 50 0.005349
3506.0 49 0.005242
1680.0 48 0.005135
3597.0 48 0.005135
1833.0 47 0.005028
9015.0 47 0.005028
10081.0 47 0.005028
3355.0 46 0.004921
9441.0 46 0.004921
1315.0 46 0.004921
4085.0 45 0.004814
9380.0 45 0.004814
5270.0 45 0.004814
10507.0 45 0.004814
4115.0 44 0.004707
1253.0 43 0.004600
9472.0 43 0.004600
9896.0 42 0.004493
1710.0 41 0.004386
9350.0 40 0.004279
9564.0 40 0.004279
10596.0 39 0.004172
1224.0 39 0.004172
3536.0 39 0.004172
10166.0 38 0.004065
4024.0 38 0.004065
9929.0 38 0.004065
10476.0 37 0.003958
9199.0 37 0.003958
3202.0 37 0.003958
3171.0 36 0.003851
10749.0 36 0.003851
3294.0 35 0.003744
1284.0 35 0.003744
5393.0 35 0.003744
3962.0 35 0.003744
9836.0 34 0.003637
3385.0 34 0.003637
1163.0 34 0.003637
10443.0 34 0.003637
1132.0 34 0.003637
3324.0 33 0.003530
3567.0 33 0.003530
4451.0 33 0.003530
1529.0 32 0.003423
10694.0 32 0.003423
1193.0 32 0.003423
1345.0 31 0.003316
4602.0 31 0.003316
10774.0 30 0.003209
1406.0 30 0.003209
5242.0 30 0.003209
1771.0 30 0.003209
3444.0 30 0.003209
3720.0 30 0.003209
1894.0 30 0.003209
7703.0 29 0.003102
1376.0 29 0.003102
4206.0 29 0.003102
3840.0 28 0.002995
4571.0 27 0.002888
3901.0 27 0.002888
10964.0 27 0.002888
10294.0 27 0.002888
1649.0 27 0.002888
5211.0 27 0.002888
5301.0 26 0.002781
5058.0 26 0.002781
5089.0 26 0.002781
7700.0 26 0.002781
11051.0 26 0.002781
4816.0 25 0.002674
4298.0 25 0.002674
4267.0 25 0.002674
4359.0 25 0.002674
3871.0 25 0.002674
10869.0 24 0.002567
5515.0 24 0.002567
3750.0 24 0.002567
5576.0 24 0.002567
5636.0 23 0.002460
3689.0 23 0.002460
1071.0 23 0.002460
10203.0 22 0.002353
10960.0 22 0.002353
5028.0 21 0.002246
4632.0 21 0.002246
3475.0 20 0.002140
1498.0 20 0.002140
5362.0 20 0.002140
5485.0 20 0.002140
4054.0 20 0.002140
4328.0 20 0.002140
3628.0 19 0.002033
5607.0 19 0.002033
5728.0 19 0.002033
11359.0 18 0.001926
10142.0 18 0.001926
4785.0 18 0.001926
6428.0 18 0.001926
883.0 18 0.001926
4693.0 18 0.001926
5150.0 18 0.001926
11206.0 17 0.001819
10743.0 17 0.001819
5820.0 17 0.001819
5454.0 17 0.001819
4724.0 17 0.001819
4936.0 17 0.001819
5181.0 17 0.001819
11086.0 16 0.001712
10834.0 16 0.001712
10537.0 16 0.001712
6307.0 16 0.001712
4967.0 16 0.001712
6093.0 16 0.001712
10718.0 15 0.001605
6216.0 15 0.001605
10599.0 15 0.001605
5546.0 15 0.001605
8589.0 14 0.001498
11139.0 14 0.001498
10415.0 14 0.001498
11176.0 14 0.001498
11145.0 14 0.001498
5973.0 14 0.001498
5759.0 13 0.001391
6550.0 13 0.001391
5120.0 13 0.001391
5667.0 13 0.001391
9898.0 13 0.001391
11025.0 13 0.001391
10721.0 13 0.001391
6124.0 12 0.001284
8529.0 12 0.001284
10050.0 12 0.001284
6277.0 12 0.001284
6154.0 11 0.001177
5942.0 11 0.001177
4663.0 11 0.001177
6246.0 11 0.001177
5881.0 10 0.001070
6032.0 10 0.001070
5789.0 10 0.001070
5698.0 10 0.001070
10929.0 10 0.001070
4997.0 10 0.001070
6458.0 9 0.000963
10841.0 9 0.000963
10780.0 9 0.000963
10624.0 9 0.000963
6519.0 9 0.000963
6001.0 8 0.000856
11329.0 8 0.000856
10568.0 8 0.000856
11098.0 8 0.000856
10805.0 8 0.000856
11419.0 7 0.000749
5851.0 7 0.000749
11224.0 7 0.000749
10887.0 7 0.000749
10652.0 7 0.000749
5912.0 7 0.000749
10811.0 6 0.000642
11163.0 6 0.000642
6489.0 6 0.000642
5.0 6 0.000642
11510.0 6 0.000642
6185.0 6 0.000642
6063.0 6 0.000642
10172.0 5 0.000535
9837.0 5 0.000535
10629.0 5 0.000535
11857.0 5 0.000535
10295.0 5 0.000535
10690.0 4 0.000428
11079.0 4 0.000428
11856.0 4 0.000428
11542.0 4 0.000428
11114.0 4 0.000428
10446.0 4 0.000428
7667.0 4 0.000428
10933.0 3 0.000321
11267.0 3 0.000321
11633.0 3 0.000321
11029.0 3 0.000321
6397.0 3 0.000321
11237.0 3 0.000321
9806.0 3 0.000321
11285.0 3 0.000321
11695.0 3 0.000321
37.0 3 0.000321
11826.0 3 0.000321
11298.0 2 0.000214
248.0 2 0.000214
11479.0 2 0.000214
11567.0 2 0.000214
11055.0 2 0.000214
11076.0 2 0.000214
10660.0 2 0.000214
463.0 2 0.000214
796.0 2 0.000214
11664.0 2 0.000214
11358.0 2 0.000214
11238.0 2 0.000214
7690.0 2 0.000214
11772.0 2 0.000214
11103.0 2 0.000214
10902.0 2 0.000214
11370.0 2 0.000214
11572.0 1 0.000107
8698.0 1 0.000107
11603.0 1 0.000107
9561.0 1 0.000107
11476.0 1 0.000107
11867.0 1 0.000107
11723.0 1 0.000107
10572.0 1 0.000107
11631.0 1 0.000107
767.0 1 0.000107
11888.0 1 0.000107
11968.0 1 0.000107
705.0 1 0.000107
11450.0 1 0.000107
11937.0 1 0.000107
11873.0 1 0.000107
8731.0 1 0.000107
11420.0 1 0.000107
1734.0 1 0.000107
11793.0 1 0.000107
10994.0 1 0.000107
11037.0 1 0.000107
11022.0 1 0.000107
219.0 1 0.000107
11053.0 1 0.000107
11026.0 1 0.000107
310.0 1 0.000107
67.0 1 0.000107
949.0 1 0.000107
7697.0 1 0.000107
198.0 1 0.000107
1006.0 1 0.000107
432.0 1 0.000107
msf_recencyrecurringdonorcont__c: numero de dias desde la ultima aportacion de socio recurrente.
Se puede observar que hay un 48% de registros a vacio.
Analsis de distribución por variables
-> msf_recencytotalcont__c: Variable numerica
In [283]:
# Vamos a realizar analisis por cada variable
var = "msf_recencytotalcont__c"
In [284]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_recencytotalcont__c es 507444. Lo que supone un 28.13788698023033%
El nº de vacios para la variable msf_recencytotalcont__c es 0. Lo que supone un 0.0%
Out[284]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_recencydonorcont__c',
 'msf_recencyrecurringdonorcont__c',
 'msf_recencytotalcont__c']
In [285]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[285]:
# Tot % Tot
4.0 393331 30.350200
36.0 21608 1.667316
66.0 20879 1.611065
186.0 13454 1.038137
156.0 12853 0.991763
... ... ...
4486.0 1 0.000077
4479.0 1 0.000077
10268.0 1 0.000077
11742.0 1 0.000077
3421.0 1 0.000077

10599 rows × 2 columns

msf_recencytotalcont__c: numero de dias desde la ultima aportacion.
Se puede observar que tiene un 5% de nulos, al ser de ultima fecha no aporta informacion.
Analsis de distribución por variables
-> msf_PercomsSummary__c: Variable categorica
In [286]:
# Vamos a realizar analisis por cada variable
var = "msf_percomssummary__c"
In [287]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_percomssummary__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_percomssummary__c es 1. Lo que supone un 5.545023092248668e-05%
In [288]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[288]:
# Tot % Tot
Todo 1205184 66.827731
Varios 314074 17.415476
Nada 203118 11.262940
No captación de fondos 80975 4.490082
Sólo certificado fiscal 67 0.003715
1 0.000055
msf_percomssummary__c: permiso de comunicación.
Se puede observar que casi no hay vacios. Se trabajará con aquellos que tienen o la variable informada a Todo o a Varios.
Analsis de distribución por variables
-> msf_scoringrfvdonor__c: Variable numerica
In [289]:
# Vamos a realizar analisis por cada variable
var = "msf_scoringrfvdonor__c"
In [290]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_scoringrfvdonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_scoringrfvdonor__c es 0. Lo que supone un 0.0%
In [291]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[291]:
# Tot % Tot
0.0 1177448 65.289763
1.0 85687 4.751364
1.5 57575 3.192547
1.8 57371 3.181235
1.6 53844 2.985662
1.2 50582 2.804784
1.4 46243 2.564185
1.7 23383 1.296593
1.9 20393 1.130797
2.5 18950 1.050782
2.0 18407 1.020672
3.0 18242 1.011523
2.3 18030 0.999768
2.1 15769 0.874395
2.2 13167 0.730113
2.8 11864 0.657862
2.4 9239 0.512305
3.5 9205 0.510419
3.3 9097 0.504431
3.2 8156 0.452252
2.6 7530 0.417540
3.8 7305 0.405064
2.7 6664 0.369520
3.6 5507 0.305364
4.1 4687 0.259895
3.4 4637 0.257123
3.7 4495 0.249249
4.0 4386 0.243205
2.9 4207 0.233279
3.9 3776 0.209380
3.1 3762 0.208604
1.3 3613 0.200342
4.2 2944 0.163245
4.4 2588 0.143505
4.3 2578 0.142951
4.5 2033 0.112730
4.6 1599 0.088665
4.8 1420 0.078739
4.7 1276 0.070754
5.0 1170 0.064877
4.9 887 0.049184
5.1 711 0.039425
5.2 446 0.024731
5.5 354 0.019629
5.4 343 0.019019
5.3 310 0.017190
0.2 224 0.012421
5.7 183 0.010147
5.6 181 0.010036
6.0 178 0.009870
5.8 93 0.005157
5.9 87 0.004824
0.4 86 0.004769
6.2 66 0.003660
0.6 65 0.003604
6.4 65 0.003604
0.8 58 0.003216
6.5 58 0.003216
6.1 57 0.003161
0.5 40 0.002218
6.6 22 0.001220
0.7 21 0.001164
7.0 20 0.001109
6.3 11 0.000610
0.9 9 0.000499
6.8 7 0.000388
6.7 5 0.000277
1.1 3 0.000166
msf_scoringrfvdonor__c: scoring donante.
Se puede observar que hay muchos a 0% ya que los donantes recurrentes de esta tabla, ya se ha analizado que el 73% no hace donaciones puntuales por lo que no tiene score en este apartado.
Analsis de distribución por variables
-> msf_scoringrfvrecurringdonor__c: Variable numerica
In [292]:
# Vamos a realizar analisis por cada variable
var = "msf_scoringrfvrecurringdonor__c"
In [293]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_scoringrfvrecurringdonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_scoringrfvrecurringdonor__c es 0. Lo que supone un 0.0%
In [294]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[294]:
# Tot % Tot
0.0 867770 48.118047
5.0 131141 7.271799
4.5 97173 5.388265
3.5 76494 4.241610
0.4 57835 3.206964
0.2 43619 2.418684
3.0 38256 2.121304
0.6 32846 1.821318
1.9 29234 1.621032
2.1 28489 1.579722
1.7 27945 1.549557
1.0 27282 1.512793
0.8 25872 1.434608
0.5 21279 1.179925
0.7 21045 1.166950
2.0 20994 1.164122
4.7 20535 1.138670
4.2 18389 1.019674
1.4 18145 1.006144
1.5 15185 0.842012
1.8 14527 0.805526
0.9 14394 0.798151
2.5 13841 0.767487
1.6 13796 0.764991
3.2 13232 0.733717
1.1 13128 0.727951
2.3 11952 0.662741
5.5 11441 0.634406
4.0 11273 0.625090
1.2 11248 0.623704
1.3 9431 0.522951
4.4 9403 0.521399
3.9 8215 0.455524
2.9 5830 0.323275
2.7 5153 0.285735
2.2 4636 0.257067
2.4 2281 0.126482
6.0 2103 0.116612
3.7 1697 0.094099
3.6 1357 0.075246
4.1 1343 0.074470
2.6 1061 0.058833
3.4 753 0.041754
5.2 752 0.041699
4.9 371 0.020572
3.1 146 0.008096
5.7 143 0.007929
6.5 92 0.005101
4.6 75 0.004159
5.4 67 0.003715
2.8 45 0.002495
3.3 31 0.001719
4.3 25 0.001386
3.8 18 0.000998
5.1 12 0.000665
6.2 6 0.000333
4.8 4 0.000222
5.9 3 0.000166
7.0 2 0.000111
5.6 2 0.000111
6.1 1 0.000055
6.7 1 0.000055
msf_scoringrfvrecurringdonor__c: scoring donante recurrente.
Se puede observar que no hay vacios. Hay buena distribucion y puede ser buena variable, se incluirá en e modelo, pero deberia poder ser dinamica, teniendo una cada año al menos en funcion de su evolucion.
Analsis de distribución por variables
-> msf_scoringrvtotal__c: Variable numerica
In [295]:
# Vamos a realizar analisis por cada variable
var = "msf_scoringrvtotal__c"
In [296]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_scoringrvtotal__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_scoringrvtotal__c es 0. Lo que supone un 0.0%
In [297]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[297]:
# Tot % Tot
0.0 506626 28.092529
5.0 204141 11.319666
4.2 189206 10.491516
1.8 155567 8.626226
2.6 123064 6.823927
1.0 94838 5.258789
3.4 70635 3.916727
1.2 62393 3.459706
1.6 54394 3.016160
1.4 46581 2.582927
2.0 43373 2.405043
3.6 34596 1.918356
2.2 31964 1.772411
4.4 30610 1.697332
4.6 28814 1.597743
3.8 28472 1.578779
5.8 22880 1.268701
2.4 11962 0.663296
4.8 11208 0.621486
2.8 11205 0.621320
4.0 9431 0.522951
3.0 8282 0.459239
6.6 6257 0.346952
3.2 4101 0.227401
5.2 3544 0.196516
5.4 2623 0.145446
6.0 1654 0.091715
5.6 1282 0.071087
6.2 1042 0.057779
7.4 860 0.047687
6.4 527 0.029222
6.8 326 0.018077
7.0 161 0.008927
7.6 151 0.008373
8.2 137 0.007597
0.8 122 0.006765
7.2 109 0.006044
7.8 87 0.004824
0.6 55 0.003050
0.2 49 0.002717
0.4 47 0.002606
8.0 43 0.002384
msf_scoringrvtotal__c: scoring total.
Se puede observar que no hay vacios. Hay buena distribucion y puede ser buena variable, se incluirá en e modelo, pero deberia poder ser dinamica, teniendo una cada año al menos en funcion de su evolucion.
Analsis de distribución por variables
-> msf_mailingsegment__c: Variable categorica
In [298]:
# Vamos a realizar analisis por cada variable
var = "msf_mailingsegment__c"
In [299]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_mailingsegment__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_mailingsegment__c es 8. Lo que supone un 0.0004436018473798934%
In [300]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[300]:
# Tot % Tot
No se está calculando la cadencia de donante 431891 23.948456
SOC NO REC SIN EXTRA 313671 17.393129
BAJAS ANTIGUAS 154299 8.555915
DON MUY ANTIGUOS 150956 8.370545
BAJAS MUY ANTIGUAS 134593 7.463213
BAJAS NO REC 126262 7.001257
BAJAS ACT 50271 2.787539
DON ANTIGUOS 48193 2.672313
SOC CON EXTRA ACT 47970 2.659948
BAJAS REC 41715 2.313106
DON UNICO REC 40457 2.243350
SOC CON EXTRA NO REC 38684 2.145037
EMPRESAS NO SOCIAS 36971 2.050050
DON 1R AÑO 36068 1.999979
SOC NUEVOS 29424 1.631568
SOC CON EXTRA REC 28789 1.596357
SOC REC SIN EXTRA 21233 1.177375
DON UNICO NO REC 20670 1.146156
DON PS ACT 12401 0.687638
DON OCA ACT 9834 0.545298
DON OCA REC 9354 0.518681
DON OCA NO REC 9196 0.509920
DON PS NO REC 4809 0.266660
DON PS REC 2691 0.149217
EMPRESAS SOCIAS 2381 0.132027
No cumple ninguno de los criterios anteriores 628 0.034823
8 0.000444
msf_mailingsegment__c: segmento colaborador.
Se puede observar que casi no existen los vacios.
Analsis de distribución por variables
-> msf_membertype__c: Variable categorica
In [301]:
# Vamos a realizar analisis por cada variable
var = "msf_membertype__c"
In [302]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_membertype__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_membertype__c es 0. Lo que supone un 0.0%
In [303]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[303]:
# Tot % Tot
Baja 425726 23.606605
Nada 413729 22.941369
Socio 301335 16.709095
Exdonante 300207 16.646547
Socio + Exdonante 132714 7.359022
Baja + Exdonante 79431 4.404467
Donante 59189 3.282044
Socio + Donante 48175 2.671315
Nada (Donante SMS) 36990 2.051104
Baja + Donante 5923 0.328432
msf_membertype__c: tipo de miembro.
Se puede observar que no hay vacios.
Analsis de distribución por variables
-> npo02__totaloppamount__c: Variable numerica
In [304]:
# Vamos a realizar analisis por cada variable
var = "npo02__totaloppamount__c"
In [305]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__totaloppamount__c es 1. Lo que supone un 5.545023092248668e-05%
El nº de vacios para la variable npo02__totaloppamount__c es 0. Lo que supone un 0.0%
In [306]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[306]:
# Tot % Tot
0.00 506559 28.088829
1.00 47263 2.620746
10.00 29492 1.635339
30.00 26316 1.459229
20.00 24710 1.370176
... ... ...
8584.23 1 0.000055
1277.43 1 0.000055
3447.32 1 0.000055
467.54 1 0.000055
1628.70 1 0.000055

100601 rows × 2 columns

npo02__totaloppamount__c: total donado.
Se puede observar que hay un 5% de nulos y un 28% de valores a 0. Aunque sobre los socios objetivo es nulo.
Analsis de distribución por variables
-> npo02__oppamountthisyear__c: Variable numerica
In [307]:
# Vamos a realizar analisis por cada variable
var = "npo02__oppamountthisyear__c"
In [308]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__oppamountthisyear__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__oppamountthisyear__c es 0. Lo que supone un 0.0%
In [309]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[309]:
# Tot % Tot
0.0 1803419 100.0
npo02__OppAmountThisYear__c: importe total de aportaciones al año que realizó este año.
Se puede observar que no hay vacios. Pero todos los valores e informan a 0.
Analsis de distribución por variables
-> npo02__oppamount2yearsago__c: Variable numerica
In [310]:
# Vamos a realizar analisis por cada variable
var = "npo02__oppamount2yearsago__c"
In [311]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__oppamount2yearsago__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__oppamount2yearsago__c es 0. Lo que supone un 0.0%
In [312]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[312]:
# Tot % Tot
0.0 1803419 100.0
npo02__oppamount2yearsago__c: importe total de aportaciones al año que realizó hace 2 años.
Se puede observar que no hay vacios. Pero todos los valores e informan a 0.
Analsis de distribución por variables
-> npo02__oppamountlastyear__c: Variable numerica
In [313]:
# Vamos a realizar analisis por cada variable
var = "npo02__oppamountlastyear__c"
In [314]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__oppamountlastyear__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__oppamountlastyear__c es 0. Lo que supone un 0.0%
In [315]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[315]:
# Tot % Tot
0.0 1803419 100.0
npo02__oppamountlastyear__c: importe total de aportaciones al año que realizó el año pasado.
Se puede observar que no hay vacios. Pero todos los valores e informan a 0.
Analsis de distribución por variables
-> npo02__best_gift_year_total__c: Variable numerica
In [316]:
# Vamos a realizar analisis por cada variable
var = "npo02__best_gift_year_total__c"
In [317]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__best_gift_year_total__c es 507444. Lo que supone un 28.13788698023033%
El nº de vacios para la variable npo02__best_gift_year_total__c es 0. Lo que supone un 0.0%
Out[317]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_recencydonorcont__c',
 'msf_recencyrecurringdonorcont__c',
 'msf_recencytotalcont__c',
 'npo02__best_gift_year_total__c']
In [318]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[318]:
# Tot % Tot
120.00 119005 9.182662
60.00 69156 5.336214
180.00 58640 4.524779
1.00 49590 3.826463
240.00 39480 3.046355
... ... ...
13095.68 1 0.000077
528.49 1 0.000077
275.25 1 0.000077
254.08 1 0.000077
161.01 1 0.000077

15457 rows × 2 columns

npo02__best_gift_year_total__c: importe total de aportaciones al año que más ha aportado.
Se puede observar que hay un 28% de nulos. Aunque sobre los socios objetivo este porcentaje es menor.
Analsis de distribución por variables
-> msf_totalfiscaloppamount__c: Variable numerica
In [319]:
# Vamos a realizar analisis por cada variable
var = "msf_totalfiscaloppamount__c"
In [320]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_totalfiscaloppamount__c es 3. Lo que supone un 0.00016635069276746005%
El nº de vacios para la variable msf_totalfiscaloppamount__c es 0. Lo que supone un 0.0%
In [321]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[321]:
# Tot % Tot
0.00 506572 28.089581
1.00 47198 2.617144
10.00 29567 1.639500
30.00 26334 1.460229
20.00 24746 1.372174
... ... ...
3220.59 1 0.000055
1225.69 1 0.000055
2802.82 1 0.000055
12141.44 1 0.000055
1628.70 1 0.000055

100336 rows × 2 columns

msf_totalfiscaloppamount__c: importe total de aportaciones fiscal cobradas.
Se puede observar que casi no existen vacios ni nulos, aunque un 28% de los registros están a 0%
Analsis de distribución por variables
-> msf_lastannualizedquota__c: Variable numerica
In [322]:
# Vamos a realizar analisis por cada variable
var = "msf_lastannualizedquota__c"
In [323]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lastannualizedquota__c es 850655. Lo que supone un 47.1690161853679%
El nº de vacios para la variable msf_lastannualizedquota__c es 0. Lo que supone un 0.0%
Out[323]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_recencydonorcont__c',
 'msf_recencyrecurringdonorcont__c',
 'msf_recencytotalcont__c',
 'npo02__best_gift_year_total__c',
 'msf_lastannualizedquota__c']
In [324]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[324]:
# Tot % Tot
1.200000e+02 205240 21.541536
1.800000e+02 103733 10.887586
6.000000e+01 96713 10.150782
2.400000e+02 66814 7.012650
1.440000e+02 51175 5.371215
7.200000e+01 35993 3.777746
3.600000e+02 25096 2.634021
3.000000e+02 24793 2.602218
3.600000e+01 22838 2.397026
9.600000e+01 20117 2.111436
8.400000e+01 16966 1.780714
1.680000e+02 16565 1.738626
1.000000e+02 14049 1.474552
7.212000e+01 11666 1.224438
5.000000e+01 9134 0.958684
2.040000e+02 8534 0.895710
4.000000e+01 8400 0.881645
8.000000e+01 7842 0.823079
6.000000e+02 7823 0.821085
2.000000e+02 7589 0.796525
2.000000e+01 7526 0.789912
4.800000e+02 6982 0.732815
2.160000e+02 6854 0.719381
3.000000e+01 6570 0.689573
1.320000e+02 6168 0.647380
1.560000e+02 6143 0.644756
1.500000e+02 5878 0.616942
1.080000e+02 5724 0.600778
1.920000e+02 5646 0.592592
4.800000e+01 5377 0.564358
4.200000e+02 5289 0.555122
3.120000e+02 4814 0.505267
2.640000e+02 4417 0.463599
1.200000e+01 4115 0.431901
5.196000e+01 3794 0.398210
6.010000e+01 3638 0.381836
1.202000e+02 3425 0.359480
2.280000e+02 3416 0.358536
1.000000e+01 3402 0.357066
1.600000e+02 3319 0.348355
7.200000e+02 3191 0.334920
3.005000e+01 3140 0.329567
2.760000e+02 2810 0.294931
9.000000e+01 2692 0.282546
1.442400e+02 2618 0.274779
1.500000e+01 2532 0.265753
1.400000e+02 2396 0.251479
2.163600e+02 2215 0.232481
7.000000e+01 2195 0.230382
3.606000e+02 2183 0.229123
4.000000e+02 1865 0.195746
3.840000e+02 1834 0.192493
1.200000e+03 1833 0.192388
2.500000e+01 1709 0.179373
5.400000e+02 1644 0.172551
2.880000e+02 1527 0.160271
7.500000e+01 1462 0.153448
2.400000e+01 1415 0.148515
2.520000e+02 1318 0.138334
3.240000e+02 1265 0.132772
2.500000e+02 1243 0.130463
3.360000e+02 1227 0.128783
3.000000e+00 1158 0.121541
1.803000e+01 1102 0.115663
2.600000e+02 1070 0.112305
9.015000e+01 1023 0.107372
2.404000e+02 1008 0.105797
3.960000e+02 827 0.086800
5.000000e+00 812 0.085226
1.300000e+02 770 0.080817
5.000000e+02 757 0.079453
1.100000e+02 752 0.078928
2.800000e+02 737 0.077354
2.200000e+02 676 0.070951
1.250000e+02 657 0.068957
3.500000e+01 646 0.067803
8.400000e+02 641 0.067278
6.600000e+02 641 0.067278
3.200000e+02 604 0.063395
4.500000e+01 599 0.062870
1.800000e+01 516 0.054158
4.808000e+01 492 0.051639
7.212000e+02 487 0.051114
0.000000e+00 465 0.048805
6.500000e+01 458 0.048071
4.080000e+02 449 0.047126
9.000000e+02 445 0.046706
8.800000e+01 444 0.046601
4.320000e+02 443 0.046496
9.600000e+02 436 0.045762
1.700000e+02 431 0.045237
3.200000e+01 425 0.044607
4.200000e+01 397 0.041668
1.502500e+02 395 0.041458
2.800000e+01 385 0.040409
2.100000e+02 373 0.039149
1.000000e+03 367 0.038520
5.500000e+01 366 0.038415
7.800000e+02 361 0.037890
5.200000e+01 359 0.037680
5.600000e+01 341 0.035791
4.440000e+02 340 0.035686
3.500000e+02 339 0.035581
2.404000e+01 336 0.035266
3.720000e+02 328 0.034426
6.240000e+02 324 0.034006
5.040000e+02 319 0.033482
1.750000e+02 316 0.033167
8.000000e+02 294 0.030858
3.606000e+01 289 0.030333
1.080000e+03 282 0.029598
8.500000e+01 274 0.028758
1.650000e+02 274 0.028758
1.120000e+02 268 0.028129
1.081200e+02 257 0.026974
2.200000e+01 253 0.026554
2.300000e+02 245 0.025715
6.000000e+00 244 0.025610
3.480000e+02 241 0.025295
1.800000e+03 239 0.025085
4.560000e+02 236 0.024770
5.200000e+02 231 0.024245
9.200000e+01 231 0.024245
1.040400e+02 195 0.020467
1.802400e+02 192 0.020152
2.884800e+02 192 0.020152
1.050000e+02 189 0.019837
6.800000e+01 189 0.019837
1.040000e+02 174 0.018263
1.520000e+02 170 0.017843
6.400000e+01 170 0.017843
1.600000e+01 167 0.017528
3.485000e+01 165 0.017318
1.400000e+01 165 0.017318
2.400000e+03 163 0.017108
1.280000e+02 162 0.017003
9.616000e+01 162 0.017003
5.160000e+02 157 0.016478
3.400000e+02 156 0.016373
4.400000e+02 155 0.016268
1.803000e+02 152 0.015954
1.350000e+02 151 0.015849
1.202000e+01 145 0.015219
3.486000e+01 143 0.015009
1.730400e+02 142 0.014904
1.240000e+02 141 0.014799
1.900000e+02 140 0.014694
5.280000e+02 140 0.014694
5.400000e+01 140 0.014694
5.520000e+02 139 0.014589
2.240000e+02 138 0.014484
1.394000e+02 137 0.014379
8.640000e+02 135 0.014169
1.440000e+03 135 0.014169
1.150000e+02 134 0.014064
6.200000e+01 134 0.014064
2.250000e+02 133 0.013959
6.010000e+00 130 0.013645
1.700000e+01 124 0.013015
4.400000e+01 124 0.013015
4.500000e+02 124 0.013015
1.039200e+02 122 0.012805
1.500000e+03 116 0.012175
1.480000e+02 112 0.011755
1.394400e+02 111 0.011650
7.224000e+01 110 0.011545
2.700000e+02 110 0.011545
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4.620000e+02 1 0.000105
2.164000e+01 1 0.000105
2.404400e+02 1 0.000105
2.721200e+02 1 0.000105
2.640000e+03 1 0.000105
3.028800e+02 1 0.000105
6.015000e+01 1 0.000105
5.580000e+02 1 0.000105
1.804000e+01 1 0.000105
2.018400e+02 1 0.000105
1.221200e+02 1 0.000105
3.110000e+02 1 0.000105
5.196000e+02 1 0.000105
3.020000e+03 1 0.000105
9.999000e+01 1 0.000105
1.141800e+02 1 0.000105
3.740000e+02 1 0.000105
1.410000e+02 1 0.000105
3.230000e+02 1 0.000105
1.240800e+02 1 0.000105
4.080000e+03 1 0.000105
9.680000e+02 1 0.000105
5.908000e+01 1 0.000105
5.160000e+01 1 0.000105
3.003600e+02 1 0.000105
3.363600e+02 1 0.000105
2.080800e+02 1 0.000105
1.430000e+02 1 0.000105
3.750000e+01 1 0.000105
2.884900e+02 1 0.000105
7.788000e+02 1 0.000105
2.260000e+02 1 0.000105
1.892400e+02 1 0.000105
5.408000e+01 1 0.000105
1.402000e+01 1 0.000105
2.800000e+03 1 0.000105
2.242400e+02 1 0.000105
1.382300e+02 1 0.000105
4.928400e+02 1 0.000105
2.524000e+02 1 0.000105
4.808100e+02 1 0.000105
1.009680e+03 1 0.000105
5.560000e+02 1 0.000105
9.810000e+01 1 0.000105
1.501500e+02 1 0.000105
1.658800e+02 1 0.000105
3.850000e+02 1 0.000105
4.500000e+03 1 0.000105
6.040000e+02 1 0.000105
5.507500e+02 1 0.000105
1.522400e+02 1 0.000105
9.096000e+01 1 0.000105
1.002000e+02 1 0.000105
9.015100e+02 1 0.000105
4.189200e+02 1 0.000105
1.215000e+03 1 0.000105
1.027200e+02 1 0.000105
7.320000e+01 1 0.000105
4.840000e+02 1 0.000105
1.512000e+03 1 0.000105
1.268400e+02 1 0.000105
1.297200e+02 1 0.000105
1.110000e+03 1 0.000105
2.804000e+01 1 0.000105
2.253500e+02 1 0.000105
3.002000e+02 1 0.000105
3.334800e+02 1 0.000105
7.980000e+02 1 0.000105
2.810000e+02 1 0.000105
9.016000e+01 1 0.000105
2.884800e+03 1 0.000105
1.002000e+03 1 0.000105
2.115200e+02 1 0.000105
7.680000e+01 1 0.000105
9.075000e+01 1 0.000105
2.642400e+02 1 0.000105
3.005060e+04 1 0.000105
1.520000e+03 1 0.000105
7.513000e+01 1 0.000105
3.244800e+02 1 0.000105
4.094400e+02 1 0.000105
1.051000e+02 1 0.000105
8.240000e+02 1 0.000105
3.050000e+01 1 0.000105
1.634000e+02 1 0.000105
2.604000e+02 1 0.000105
7.001000e+01 1 0.000105
2.402000e+02 1 0.000105
1.471200e+02 1 0.000105
1.200400e+02 1 0.000105
6.001000e+01 1 0.000105
1.350000e+03 1 0.000105
4.207100e+02 1 0.000105
1.252000e+02 1 0.000105
5.050000e+02 1 0.000105
7.813200e+02 1 0.000105
3.604800e+02 1 0.000105
8.212000e+01 1 0.000105
1.485000e+03 1 0.000105
7.620000e+01 1 0.000105
5.528000e+01 1 0.000105
1.680000e+01 1 0.000105
1.806000e+01 1 0.000105
9.080000e+00 1 0.000105
4.327000e+01 1 0.000105
2.668800e+02 1 0.000105
1.442000e+02 1 0.000105
1.250100e+02 1 0.000105
2.632000e+01 1 0.000105
9.000000e+03 1 0.000105
7.933200e+02 1 0.000105
4.020000e+02 1 0.000105
1.502530e+03 1 0.000105
1.080000e+06 1 0.000105
1.239600e+02 1 0.000105
3.907000e+01 1 0.000105
4.399200e+02 1 0.000105
7.280000e+02 1 0.000105
5.320000e+02 1 0.000105
2.472000e+02 1 0.000105
1.253300e+02 1 0.000105
1.752000e+03 1 0.000105
2.058000e+04 1 0.000105
3.400800e+02 1 0.000105
1.164000e+06 1 0.000105
2.700500e+02 1 0.000105
2.730000e+02 1 0.000105
5.400000e+03 1 0.000105
5.949600e+02 1 0.000105
1.104000e+02 1 0.000105
3.820800e+02 1 0.000105
1.600800e+02 1 0.000105
1.141900e+02 1 0.000105
1.690000e+02 1 0.000105
1.382000e+01 1 0.000105
2.500000e+04 1 0.000105
4.380000e+02 1 0.000105
1.642400e+02 1 0.000105
2.328000e+03 1 0.000105
6.972000e+01 1 0.000105
4.020000e+01 1 0.000105
1.441200e+02 1 0.000105
7.220000e+00 1 0.000105
7.710000e+01 1 0.000105
1.280100e+02 1 0.000105
2.956920e+03 1 0.000105
7.230000e+01 1 0.000105
1.990000e+02 1 0.000105
1.950000e+01 1 0.000105
1.750000e+03 1 0.000105
5.750000e+02 1 0.000105
4.580000e+02 1 0.000105
1.200000e+05 1 0.000105
4.688400e+02 1 0.000105
1.536000e+03 1 0.000105
2.704500e+02 1 0.000105
1.261500e+02 1 0.000105
1.117920e+03 1 0.000105
1.502400e+02 1 0.000105
2.103200e+02 1 0.000105
1.081200e+03 1 0.000105
3.180000e+03 1 0.000105
4.352400e+02 1 0.000105
2.003000e+01 1 0.000105
3.065100e+02 1 0.000105
4.681200e+02 1 0.000105
7.410000e+02 1 0.000105
1.762400e+02 1 0.000105
1.710000e+02 1 0.000105
5.944800e+02 1 0.000105
1.992000e+03 1 0.000105
9.060000e+02 1 0.000105
4.510000e+02 1 0.000105
6.490000e+01 1 0.000105
3.780000e+02 1 0.000105
7.208000e+02 1 0.000105
3.588000e+01 1 0.000105
1.502400e+03 1 0.000105
1.330000e+02 1 0.000105
2.043600e+02 1 0.000105
1.333200e+02 1 0.000105
8.656000e+01 1 0.000105
5.120000e+02 1 0.000105
1.513200e+02 1 0.000105
4.050000e+02 1 0.000105
2.127600e+02 1 0.000105
1.443000e+02 1 0.000105
7.512700e+02 1 0.000105
6.130800e+02 1 0.000105
3.305000e+01 1 0.000105
9.010000e+01 1 0.000105
1.800000e+00 1 0.000105
1.226040e+03 1 0.000105
2.780000e+02 1 0.000105
6.080000e+02 1 0.000105
9.616000e+02 1 0.000105
5.902000e+01 1 0.000105
2.619600e+02 1 0.000105
3.205000e+02 1 0.000105
6.410000e+02 1 0.000105
6.910000e+01 1 0.000105
2.700300e+02 1 0.000105
2.401200e+02 1 0.000105
9.010000e+02 1 0.000105
1.622700e+02 1 0.000105
1.203000e+03 1 0.000105
2.901000e+02 1 0.000105
1.682400e+02 1 0.000105
2.090000e+02 1 0.000105
3.205000e+01 1 0.000105
6.300000e+02 1 0.000105
1.003200e+02 1 0.000105
2.280000e+03 1 0.000105
8.000000e+03 1 0.000105
5.440000e+02 1 0.000105
1.008000e+02 1 0.000105
1.584000e+03 1 0.000105
2.184000e+03 1 0.000105
1.116000e+03 1 0.000105
4.640000e+02 1 0.000105
2.524200e+02 1 0.000105
5.650000e+02 1 0.000105
3.480000e+03 1 0.000105
4.208000e+01 1 0.000105
7.995600e+02 1 0.000105
6.586000e+01 1 0.000105
3.612000e+03 1 0.000105
7.927200e+02 1 0.000105
2.770000e+02 1 0.000105
1.436400e+02 1 0.000105
7.809566e+09 1 0.000105
3.000000e+04 1 0.000105
1.159200e+02 1 0.000105
1.992000e+02 1 0.000105
3.300000e+03 1 0.000105
7.200000e-01 1 0.000105
3.889200e+02 1 0.000105
1.344000e+02 1 0.000105
1.834800e+02 1 0.000105
1.019600e+02 1 0.000105
1.359600e+02 1 0.000105
1.008000e+04 1 0.000105
7.640000e+02 1 0.000105
4.201000e+03 1 0.000105
2.070000e+02 1 0.000105
2.592000e+03 1 0.000105
1.044000e+03 1 0.000105
2.425000e+02 1 0.000105
4.484000e+01 1 0.000105
6.588000e+01 1 0.000105
1.983600e+02 1 0.000105
5.900000e+02 1 0.000105
1.160000e+03 1 0.000105
5.010000e+02 1 0.000105
1.399200e+02 1 0.000105
4.280000e+02 1 0.000105
7.196000e+01 1 0.000105
5.988000e+01 1 0.000105
2.410000e+02 1 0.000105
1.908000e+03 1 0.000105
2.064000e+03 1 0.000105
3.360000e+03 1 0.000105
3.602400e+02 1 0.000105
2.520000e+01 1 0.000105
2.803600e+02 1 0.000105
1.442430e+03 1 0.000105
6.006000e+01 1 0.000105
2.193600e+02 1 0.000105
3.006000e+01 1 0.000105
2.705000e+01 1 0.000105
2.308000e+02 1 0.000105
1.730000e+01 1 0.000105
3.860000e+02 1 0.000105
8.428800e+02 1 0.000105
6.132000e+01 1 0.000105
2.132000e+01 1 0.000105
2.451600e+02 1 0.000105
5.493000e+01 1 0.000105
6.600000e+03 1 0.000105
2.352000e+03 1 0.000105
4.666400e+02 1 0.000105
2.210000e+02 1 0.000105
1.752500e+02 1 0.000105
4.332000e+01 1 0.000105
9.014400e+02 1 0.000105
4.006000e+01 1 0.000105
4.059600e+02 1 0.000105
2.196000e+02 1 0.000105
1.009600e+02 1 0.000105
2.524200e+03 1 0.000105
4.447200e+02 1 0.000105
2.401000e+02 1 0.000105
3.000100e+02 1 0.000105
6.480000e+01 1 0.000105
1.153200e+02 1 0.000105
5.556000e+01 1 0.000105
2.115600e+02 1 0.000105
7.500000e+00 1 0.000105
1.121200e+02 1 0.000105
2.530000e+02 1 0.000105
2.430000e+02 1 0.000105
1.200800e+02 1 0.000105
2.598000e+01 1 0.000105
3.820000e+02 1 0.000105
2.890000e+02 1 0.000105
1.500000e+04 1 0.000105
1.810000e+02 1 0.000105
1.620500e+02 1 0.000105
1.514400e+02 1 0.000105
1.586640e+03 1 0.000105
1.930000e+02 1 0.000105
4.620000e+03 1 0.000105
1.204000e+03 1 0.000105
4.760000e+02 1 0.000105
1.939200e+02 1 0.000105
2.420000e+02 1 0.000105
8.400000e+03 1 0.000105
msf_lastannualizedquota__c: importe anualizado de la ultima cuota de socio.
Se puede observar que hay un 47% de registros a nulo. Esta campo para los socios sobre los que se va a realizar el modelo no tienen casi registros a vacio.
Analsis de distribución por variables
-> msf_valuetotalcont__c: Variable numerica
In [325]:
# Vamos a realizar analisis por cada variable
var = "msf_valuetotalcont__c"
In [326]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_valuetotalcont__c es 450726. Lo que supone un 24.99286078276873%
El nº de vacios para la variable msf_valuetotalcont__c es 0. Lo que supone un 0.0%
Out[326]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_recencydonorcont__c',
 'msf_recencyrecurringdonorcont__c',
 'msf_recencytotalcont__c',
 'npo02__best_gift_year_total__c',
 'msf_lastannualizedquota__c',
 'msf_valuetotalcont__c']
In [327]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[327]:
# Tot % Tot
120.0 119734 8.851528
60.0 89798 6.638461
180.0 61765 4.566077
0.0 56609 4.184911
30.0 55243 4.083927
... ... ...
4900.0 1 0.000074
1735.0 1 0.000074
3739.0 1 0.000074
6820.0 1 0.000074
8400.0 1 0.000074

3222 rows × 2 columns

msf_valuetotalcont__c: valor colaborador.
Hay un 25% de nulos. Se analizará si para los socios objetivo del analisis existe este porcentaje ya que si tiene donación recurrente no deberia ocurror.
Analsis de distribución por variables
-> msf_valuedonorcont__c: Variable numerica
In [328]:
# Vamos a realizar analisis por cada variable
var = "msf_valuedonorcont__c"
In [329]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_valuedonorcont__c es 1178350. Lo que supone un 65.33977960751217%
El nº de vacios para la variable msf_valuedonorcont__c es 0. Lo que supone un 0.0%
Out[329]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_recencydonorcont__c',
 'msf_recencyrecurringdonorcont__c',
 'msf_recencytotalcont__c',
 'npo02__best_gift_year_total__c',
 'msf_lastannualizedquota__c',
 'msf_valuetotalcont__c',
 'msf_valuedonorcont__c']
In [330]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[330]:
# Tot % Tot
1.00 58321 9.330330
30.00 46197 7.390704
100.00 44776 7.163369
50.00 44696 7.150571
20.00 40864 6.537518
... ... ...
99.70 1 0.000160
20.80 1 0.000160
18.42 1 0.000160
2255.78 1 0.000160
103.17 1 0.000160

11052 rows × 2 columns

msf_valuedonorcont__c: suma de las donaciones de los ultimoss 365 dias.
Se puede observar que hay un 65% de nulos. Además de un 10% de registros a 1%.
Analsis de distribución por variables
-> msf_lastyeardonorvalue__c: Variable numerica
In [331]:
# Vamos a realizar analisis por cada variable
var = "msf_lastyeardonorvalue__c"
In [332]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lastyeardonorvalue__c es 1690288. Lo que supone un 93.72685992550815%
El nº de vacios para la variable msf_lastyeardonorvalue__c es 0. Lo que supone un 0.0%
Out[332]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_recencydonorcont__c',
 'msf_recencyrecurringdonorcont__c',
 'msf_recencytotalcont__c',
 'npo02__best_gift_year_total__c',
 'msf_lastannualizedquota__c',
 'msf_valuetotalcont__c',
 'msf_valuedonorcont__c',
 'msf_lastyeardonorvalue__c']
In [333]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[333]:
# Tot % Tot
1.00 24608 21.751774
100.00 8522 7.532860
50.00 8056 7.120948
20.00 7096 6.272375
30.00 6964 6.155696
... ... ...
137.50 1 0.000884
1.06 1 0.000884
331.10 1 0.000884
1102.89 1 0.000884
296.00 1 0.000884

1229 rows × 2 columns

msf_lastyeardonorvalue__c: suma de las aportaciones de los ultimos 365 dias.
Se puede observar que hay un 94% de nulos.
Analsis de distribución por variables
-> msf_maximumdonorvalue__c: Variable numerica
In [334]:
# Vamos a realizar analisis por cada variable
var = "msf_maximumdonorvalue__c"
In [335]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_maximumdonorvalue__c es 1177577. Lo que supone un 65.29691657900909%
El nº de vacios para la variable msf_maximumdonorvalue__c es 0. Lo que supone un 0.0%
Out[335]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_recencydonorcont__c',
 'msf_recencyrecurringdonorcont__c',
 'msf_recencytotalcont__c',
 'npo02__best_gift_year_total__c',
 'msf_lastannualizedquota__c',
 'msf_valuetotalcont__c',
 'msf_valuedonorcont__c',
 'msf_lastyeardonorvalue__c',
 'msf_maximumdonorvalue__c']
In [336]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[336]:
# Tot % Tot
1.00 64616 10.324651
100.00 53088 8.482652
60.00 47996 7.669028
30.00 47179 7.538484
50.00 44401 7.094602
... ... ...
1082.79 1 0.000160
20.80 1 0.000160
2255.78 1 0.000160
101.37 1 0.000160
70.96 1 0.000160

10298 rows × 2 columns

msf_maximumdonorvalue__c: importe más elevado de todos los donativos.
Se puede observar que existe un 65% de nulos. Además de un 10% de registros a 1%.
Analsis de distribución por variables
-> msf_averagedonorvalue__c: Variable numerica
In [337]:
# Vamos a realizar analisis por cada variable
var = "msf_averagedonorvalue__c"
In [338]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_averagedonorvalue__c es 1177577. Lo que supone un 65.29691657900909%
El nº de vacios para la variable msf_averagedonorvalue__c es 0. Lo que supone un 0.0%
Out[338]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_recencydonorcont__c',
 'msf_recencyrecurringdonorcont__c',
 'msf_recencytotalcont__c',
 'npo02__best_gift_year_total__c',
 'msf_lastannualizedquota__c',
 'msf_valuetotalcont__c',
 'msf_valuedonorcont__c',
 'msf_lastyeardonorvalue__c',
 'msf_maximumdonorvalue__c',
 'msf_averagedonorvalue__c']
In [339]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[339]:
# Tot % Tot
1.00 64612 10.324011
30.00 36150 5.776218
10.00 32521 5.196359
20.00 30276 4.837643
50.00 29166 4.660282
... ... ...
536.82 1 0.000160
124.11 1 0.000160
253.67 1 0.000160
187.58 1 0.000160
897.22 1 0.000160

28206 rows × 2 columns

msf_averagedonorvalue__c: importe medio de todos los donativos.
Se puede observar que tiene un 65% de nulos. Además de un 10% de registros a 1%.
Analsis de distribución por variables
-> msf_lifetime__c: Variable numerica
In [340]:
# Vamos a realizar analisis por cada variable
var = "msf_lifetime__c"
In [341]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lifetime__c es 507098. Lo que supone un 28.11870120033115%
El nº de vacios para la variable msf_lifetime__c es 0. Lo que supone un 0.0%
Out[341]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_recencydonorcont__c',
 'msf_recencyrecurringdonorcont__c',
 'msf_recencytotalcont__c',
 'npo02__best_gift_year_total__c',
 'msf_lastannualizedquota__c',
 'msf_valuetotalcont__c',
 'msf_valuedonorcont__c',
 'msf_lastyeardonorvalue__c',
 'msf_maximumdonorvalue__c',
 'msf_averagedonorvalue__c',
 'msf_lifetime__c']
In [342]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[342]:
# Tot % Tot
0.0 431308 33.271697
1.0 91282 7.041620
2.0 68523 5.285959
3.0 64013 4.938052
6.0 61296 4.728458
7.0 59518 4.591301
4.0 58340 4.500429
5.0 58229 4.491866
8.0 56124 4.329483
9.0 39173 3.021860
10.0 32892 2.537335
11.0 31509 2.430648
12.0 29106 2.245277
13.0 26715 2.060832
14.0 24031 1.853785
17.0 19022 1.467383
18.0 18574 1.432824
16.0 17818 1.374505
15.0 16885 1.302532
19.0 14678 1.132281
20.0 11721 0.904174
28.0 10901 0.840918
23.0 9104 0.702295
22.0 7547 0.582186
24.0 6730 0.519162
21.0 6236 0.481054
29.0 5669 0.437315
25.0 5256 0.405455
26.0 4557 0.351533
27.0 4406 0.339885
30.0 3922 0.302549
31.0 739 0.057007
32.0 190 0.014657
34.0 141 0.010877
33.0 102 0.007868
35.0 50 0.003857
36.0 14 0.001080
msf_lifetime__c: numero de años enteros desde primera aportacion a la ultima.
Se puede observar que tiene un 28% de registros a vacio.
Analsis de distribución por variables
-> msf_commitment__c: Variable numerica
In [343]:
# Vamos a realizar analisis por cada variable
var = "msf_commitment__c"
In [344]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_commitment__c es 223407. Lo que supone un 12.387969739699981%
El nº de vacios para la variable msf_commitment__c es 0. Lo que supone un 0.0%
In [345]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[345]:
# Tot % Tot
0.0 1172390 74.201335
1.0 230295 14.575522
2.0 99316 6.285775
3.0 36324 2.298970
4.0 17949 1.136004
5.0 9412 0.595692
6.0 5366 0.339618
7.0 3184 0.201517
8.0 1909 0.120822
9.0 1194 0.075569
10.0 733 0.046392
11.0 527 0.033354
12.0 342 0.021645
13.0 244 0.015443
14.0 169 0.010696
15.0 123 0.007785
16.0 113 0.007152
17.0 76 0.004810
18.0 54 0.003418
19.0 41 0.002595
20.0 35 0.002215
21.0 34 0.002152
22.0 22 0.001392
23.0 20 0.001266
24.0 15 0.000949
29.0 15 0.000949
25.0 14 0.000886
26.0 11 0.000696
27.0 9 0.000570
30.0 9 0.000570
28.0 8 0.000506
32.0 7 0.000443
31.0 7 0.000443
33.0 5 0.000316
36.0 5 0.000316
43.0 4 0.000253
38.0 4 0.000253
34.0 3 0.000190
35.0 3 0.000190
61.0 2 0.000127
46.0 2 0.000127
37.0 2 0.000127
42.0 2 0.000127
80.0 1 0.000063
56.0 1 0.000063
39.0 1 0.000063
47.0 1 0.000063
57.0 1 0.000063
72.0 1 0.000063
52.0 1 0.000063
71.0 1 0.000063
83.0 1 0.000063
93.0 1 0.000063
53.0 1 0.000063
45.0 1 0.000063
54.0 1 0.000063
msf_commitment__c: suma de iteraciones.
Se puede observar que tiene un 12% de nulos, pero el 74% tiene valor 0, por lo que se descarta la variable.

3.1. Tabla contactos filtrada por Socios¶

In [346]:
# Vamos a analizar la tabla contactos
df=df_contactos[df_contactos["msf_isactiverecurringdonor__c"]=="Socio"]
In [347]:
# Se crea una lista por ahora vacia, en la que se irán añadiendo las variables que se van a eliminar del dataset por motivos varios: no utilidad, gran volumen de nulos, ...
col_to_delete_contactos_f=list()
Analsis de distribución por variables
-> msf_seniority__c: Variable numerica
In [348]:
# Vamos a realizar analisis por cada variable
var = "msf_seniority__c"
In [349]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_seniority__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_seniority__c es 0. Lo que supone un 0.0%
In [350]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[350]:
# Tot % Tot
7.0 37151 7.704096
8.0 35920 7.448820
6.0 35577 7.377692
9.0 32825 6.807003
5.0 24934 5.170626
10.0 21839 4.528808
4.0 21695 4.498947
12.0 20717 4.296136
1.0 20219 4.192865
13.0 18683 3.874341
2.0 18281 3.790977
11.0 18190 3.772106
3.0 16884 3.501277
14.0 16837 3.491531
18.0 15434 3.200587
0.0 14007 2.904667
17.0 13416 2.782110
19.0 12250 2.540313
16.0 12150 2.519576
15.0 11875 2.462549
29.0 10158 2.106490
20.0 8992 1.864694
23.0 7020 1.455755
22.0 5634 1.168337
28.0 4958 1.028153
24.0 4955 1.027531
21.0 4912 1.018614
25.0 4746 0.984190
27.0 3605 0.747578
31.0 3064 0.635389
26.0 2463 0.510758
30.0 2101 0.435690
32.0 403 0.083571
35.0 124 0.025714
34.0 92 0.019078
33.0 77 0.015968
36.0 31 0.006429
37.0 5 0.001037
msf_seniority__c: Número de años desde la fecha de su primera aportación económica hasta día de hoy.
Se puede observar que está bastante distribuido. Se analizará posteriormente si la categorización de la variable en grupos pueda dar buenos resultados.
Analsis de distribución por variables
-> npo02__best_gift_year__c: Variable numerica
In [351]:
# Vamos a realizar analisis por cada variable
var = "npo02__best_gift_year__c"
In [352]:
# Analizamos nulos
count_nulos(df_contactos,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__best_gift_year__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__best_gift_year__c es 709207. Lo que supone un 39.32569192184401%
Out[352]:
['npo02__best_gift_year__c']
In [353]:
# Analizamos posibles valores de la variable
freq_variables(df_contactos,var)
Out[353]:
# Tot % Tot
709207 39.325692
2018 303667 16.838405
2022 185032 10.260067
2021 93074 5.160975
2020 90828 5.036434
2019 77054 4.272662
2023 55899 3.099612
2010 29210 1.619701
1994 28224 1.565027
2017 21245 1.178040
2005 15932 0.883433
2014 14681 0.814065
2011 14643 0.811958
2004 13160 0.729725
2000 12659 0.701944
2015 11996 0.665181
2001 11403 0.632299
1998 11363 0.630081
2013 10940 0.606626
2016 9948 0.551619
2003 9537 0.528829
2008 8465 0.469386
1999 8142 0.451476
2009 7599 0.421366
1996 6869 0.380888
2012 6795 0.376784
2006 6723 0.372792
1992 6238 0.345899
2007 5562 0.308414
2002 4753 0.263555
1997 4491 0.249027
1995 4064 0.225350
1993 2470 0.136962
1991 624 0.034601
1989 435 0.024121
1990 212 0.011755
1988 187 0.010369
1987 88 0.004880
npo02__best_gift_year__c: Año fiscal en que se ha realizado mayor importe total.
Se puede observar que hay casi un 40% de los registros a vacio. Se analizará realziar un tratamiento de vacios.
Analsis de distribución por variables
-> msf_birthyear__c: Variable numerica
In [354]:
# Vamos a realizar analisis por cada variable
var = "msf_birthyear__c"
In [355]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_birthyear__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_birthyear__c es 110441. Lo que supone un 22.902427087826403%
Out[355]:
['npo02__best_gift_year__c', 'msf_birthyear__c']
In [356]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[356]:
# Tot % Tot
110441 22.902427
1958 10010 2.075799
1959 9924 2.057965
1957 9901 2.053195
1964 9756 2.023126
1963 9736 2.018979
1960 9652 2.001559
1962 9554 1.981237
1961 9475 1.964855
1965 9404 1.950131
1956 9080 1.882942
1966 9012 1.868841
1968 8945 1.854947
1967 8630 1.789625
1955 8335 1.728450
1973 8203 1.701077
1969 8201 1.700662
1974 8176 1.695478
1971 8175 1.695270
1972 8166 1.693404
1970 8126 1.685109
1975 7997 1.658358
1954 7816 1.620824
1976 7566 1.568980
1953 7407 1.536008
1977 7156 1.483958
1952 7085 1.469234
1978 7080 1.468197
1951 6680 1.385248
1979 6434 1.334235
1950 6378 1.322622
1949 6107 1.266424
1980 5904 1.224327
1948 5815 1.205871
1981 5542 1.149258
1947 5161 1.070250
1982 4917 1.019651
1945 4621 0.958268
1946 4605 0.954950
1983 4540 0.941471
1984 4044 0.838614
1944 3874 0.803361
1943 3842 0.796725
1985 3614 0.749444
1986 3074 0.637463
1942 2916 0.604698
1987 2851 0.591219
1940 2601 0.539376
1941 2556 0.530044
1988 2509 0.520298
1989 2316 0.480275
1990 2203 0.456842
1992 2085 0.432372
1991 2074 0.430091
1993 1986 0.411842
1994 1907 0.395459
1995 1851 0.383847
1997 1836 0.380736
1996 1824 0.378247
1999 1707 0.353985
2000 1679 0.348178
1998 1660 0.344238
1939 1630 0.338017
1938 1539 0.319146
2001 1532 0.317695
1936 1515 0.314169
1937 1413 0.293017
2002 1273 0.263985
1935 1209 0.250713
2003 1137 0.235783
1934 1016 0.210690
1933 815 0.169009
2004 738 0.153041
1932 710 0.147234
1930 558 0.115714
1931 522 0.108248
1929 265 0.054954
1928 225 0.046659
1927 168 0.034839
1926 104 0.021567
2005 103 0.021359
1925 79 0.016382
2006 73 0.015138
2017 72 0.014931
2019 60 0.012442
2016 54 0.011198
2008 52 0.010783
2020 51 0.010576
2021 49 0.010161
2014 45 0.009332
2007 44 0.009124
2015 40 0.008295
1924 40 0.008295
2013 39 0.008088
2018 34 0.007051
1923 33 0.006843
2012 31 0.006429
2009 31 0.006429
2010 31 0.006429
2011 27 0.005599
1922 23 0.004770
1921 21 0.004355
1919 19 0.003940
2022 14 0.002903
1920 13 0.002696
2023 12 0.002488
1918 7 0.001452
1917 6 0.001244
1916 5 0.001037
1902 3 0.000622
1904 3 0.000622
1915 3 0.000622
1903 2 0.000415
1908 2 0.000415
1911 2 0.000415
1906 2 0.000415
1907 2 0.000415
1900 2 0.000415
1901 1 0.000207
1912 1 0.000207
1910 1 0.000207
1905 1 0.000207
msf_birthyear__c: .
Se puede observar que hay más de un 23% de los registros a vacio. Pero como se considera una variable importante se va a incorporar al dataset haciendo tratamiento de nulos.
Analsis de distribución por variables
-> msf_entrycampaign__c: Variable string
In [357]:
# Vamos a realizar analisis por cada variable
var = "msf_entrycampaign__c"
In [358]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_entrycampaign__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_entrycampaign__c es 43. Lo que supone un 0.008917017817445834%
In [359]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[359]:
# Tot % Tot
7013Y000001mrCzQAI 18002 3.733120
7013Y000001mrBSQAY 17224 3.571784
7013Y000001mr2cQAA 13558 2.811556
7013Y000001mr2DQAQ 12301 2.550889
7013Y000001mr1MQAQ 12285 2.547571
... ... ...
7013Y000001vZryQAE 1 0.000207
7013Y000001vZvlQAE 1 0.000207
7013Y000001vaoyQAA 1 0.000207
7013Y000001rAvqQAE 1 0.000207
7013Y000001mrY3QAI 1 0.000207

2634 rows × 2 columns

msf_entrycampaign__c: .
Se puede observar que practicamente no hay vacios. Con la incorporación de información sobre el tipo de campaña puede ser util en el modelo.
Analsis de distribución por variables
-> LeadSource: Variable categorica
In [360]:
# Vamos a realizar analisis por cada variable
var = "leadsource"
In [361]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable leadsource es 0. Lo que supone un 0.0%
El nº de vacios para la variable leadsource es 0. Lo que supone un 0.0%
In [362]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[362]:
# Tot % Tot
Persona a persona 139341 28.895493
Otro 115221 23.893668
Telemarketing 82413 17.090190
Web MSF 43532 9.027340
Cupón 37592 7.795547
Personal con tablet 31661 6.565621
Teléfono campaña 13900 2.882478
Web terceros 9419 1.953242
Web campaña 3324 0.689306
Teléfono web 2438 0.505574
Teléfono SAS 1346 0.279123
Eventos 842 0.174608
Email a SAS 718 0.148893
Email a Empresas 103 0.021359
Email a Bodas 102 0.021152
Plataforma iniciativas 100 0.020737
Entidad financiera 73 0.015138
Correo postal sin cupón 60 0.012442
Teléfono Officers 20 0.004147
Teléfono Herencias y Legados 3 0.000622
Email herencias 3 0.000622
Email a One to one 3 0.000622
Email a officers Mid Donors 2 0.000415
Email a Iniciativas Solidarias 2 0.000415
Cloud page 2 0.000415
Email Director/a General 1 0.000207
SMS 1 0.000207
TelEfono officers 1 0.000207
Tel?fono SAS 1 0.000207
leadsource: Canal principal.
Se puede observar que casi no hay vacios. La mayor parte es Persona a Persona.
Analsis de distribución por variables
-> msf_firstcampaigncolaborationchannel__c: Variable categorica
In [363]:
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaigncolaborationchannel__c"
In [364]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_firstcampaigncolaborationchannel__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_firstcampaigncolaborationchannel__c es 11926. Lo que supone un 2.473124523043233%
In [365]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[365]:
# Tot % Tot
Persona a persona 135714 28.143352
Telemarketing 94523 19.601472
Otro 86148 17.864727
Web MSF 46585 9.660448
Cupón 39815 8.256536
Personal con tablet 31501 6.532441
Teléfono campaña 15706 3.256993
11926 2.473125
Web terceros 8526 1.768058
Web campaña 3027 0.627717
Teléfono web 2647 0.548915
Teléfono SAS 2027 0.420344
Email a SAS 1169 0.242418
Plataforma iniciativas 869 0.180207
Eventos 573 0.118824
web campaña 453 0.093940
Entidad financiera 438 0.090829
cupón 133 0.027581
Web MSF Mi perfil 124 0.025714
Email a Empresas 96 0.019908
Email a Bodas 86 0.017834
Correo postal sin cupón 73 0.015138
Teléfono Officers 52 0.010783
Email a officers Mid Donors 4 0.000829
Email a One to one 3 0.000622
Email a Iniciativas Solidarias 3 0.000622
Cloud page 2 0.000415
Email Director/a General 1 0.000207
msf_firstcampaigncolaborationchannel__c: Canal por el que realizó la primera donación.
Se puede observar que hay un 2% de vacios.
In [366]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("msf_firstcampaigncolaborationchannel__c")
col_to_delete_contactos
Out[366]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_recencydonorcont__c',
 'msf_recencyrecurringdonorcont__c',
 'msf_recencytotalcont__c',
 'npo02__best_gift_year_total__c',
 'msf_lastannualizedquota__c',
 'msf_valuetotalcont__c',
 'msf_valuedonorcont__c',
 'msf_lastyeardonorvalue__c',
 'msf_maximumdonorvalue__c',
 'msf_averagedonorvalue__c',
 'msf_lifetime__c',
 'msf_firstcampaigncolaborationchannel__c']
Analsis de distribución por variables
-> npo02__AverageAmount__c: Variable numerica
In [367]:
# Vamos a realizar analisis por cada variable
var = "npo02__averageamount__c"
In [368]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__averageamount__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__averageamount__c es 0. Lo que supone un 0.0%
In [369]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[369]:
# Tot % Tot
0.0 482224 100.0
npo02__averageamount__c: Media del total de aportaciones.
Se puede observar que no hay vacios pero está informado a 0 para todos los casos.
In [370]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("npo02__averageamount__c")
col_to_delete_contactos
Out[370]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_recencydonorcont__c',
 'msf_recencyrecurringdonorcont__c',
 'msf_recencytotalcont__c',
 'npo02__best_gift_year_total__c',
 'msf_lastannualizedquota__c',
 'msf_valuetotalcont__c',
 'msf_valuedonorcont__c',
 'msf_lastyeardonorvalue__c',
 'msf_maximumdonorvalue__c',
 'msf_averagedonorvalue__c',
 'msf_lifetime__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c']
Analsis de distribución por variables
-msf_isactivedonor__c: Variable categorica
In [371]:
# Vamos a realizar analisis por cada variable
var = "msf_isactivedonor__c"
In [372]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_isactivedonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_isactivedonor__c es 0. Lo que supone un 0.0%
In [373]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[373]:
# Tot % Tot
Nunca 301335 62.488595
Exdonante 132714 27.521235
Donante 48175 9.990171
msf_isactivedonor__c: donante activo
Se puede observar como la mayor parte nunca han realizado donaciones puntuales. Se puede plantear un booleano en el dataset inicial marcando como 1 a aquellos que si hayan realizado donaciones puntuales además de las periodicas y 0 en caso contrario.
Analsis de distribución por variables
-> msf_isactiverecurringdonor__c: Variable categorica
In [374]:
# Vamos a realizar analisis por cada variable
var = "msf_isactiverecurringdonor__c"
In [375]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_isactiverecurringdonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_isactiverecurringdonor__c es 0. Lo que supone un 0.0%
In [376]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[376]:
# Tot % Tot
Socio 482224 100.0
msf_isactiverecurringdonor__c: indicador de socio recurrente.
Se puede observar que no hay vacios, se usara para filtrar los registros a los que se les va a plicar el modelo.
In [377]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("msf_isactiverecurringdonor__c")
col_to_delete_contactos
Out[377]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datefirstrecurringdonorquota__c',
 'msf_datelastrecurringdonorquota__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'msf_entrydatecurrentrecurringdonor__c',
 'npsp__last_soft_credit_date__c',
 'msf_firstentrydaterecurringdonor__c',
 'npo02__firstclosedate__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'msf_ltvcont__c',
 'mailingstate',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_firstcampaignentryrecurringdonor__c',
 'msf_firstcampaingcolaboration__c',
 'msf_firstannualizedquota__c',
 'msf_recencydonorcont__c',
 'msf_recencyrecurringdonorcont__c',
 'msf_recencytotalcont__c',
 'npo02__best_gift_year_total__c',
 'msf_lastannualizedquota__c',
 'msf_valuetotalcont__c',
 'msf_valuedonorcont__c',
 'msf_lastyeardonorvalue__c',
 'msf_maximumdonorvalue__c',
 'msf_averagedonorvalue__c',
 'msf_lifetime__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactiverecurringdonor__c']
Analsis de distribución por variables
-> npsp__deceased__c: Variable categorica
In [378]:
# Vamos a realizar analisis por cada variable
var = "npsp__deceased__c"
In [379]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npsp__deceased__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npsp__deceased__c es 0. Lo que supone un 0.0%
In [380]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[380]:
# Tot % Tot
False 482136 99.981751
True 88 0.018249
npsp__deceased__c: Indicador de fallecido
Se puede observar que no hay vacios, solo el 2% han fallecido. Esto quiere decir que se podrán usar en el modelo para prdecir, pero no para aplicar.
Analsis de distribución por variables
-> msf_begindatemsf__c: Variable categorica
In [381]:
# Vamos a realizar analisis por cada variable
var = "msf_begindatemsf__c"
In [382]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_begindatemsf__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_begindatemsf__c es 0. Lo que supone un 0.0%
In [383]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[383]:
# Tot % Tot
2000-02-01 2211 0.458501
2000-01-01 1913 0.396704
2004-01-01 1749 0.362695
1995-02-01 1711 0.354814
1994-10-01 1686 0.349630
... ... ...
1992-05-24 1 0.000207
1995-11-15 1 0.000207
1990-02-12 1 0.000207
1998-11-02 1 0.000207
2011-03-26 1 0.000207

9431 rows × 2 columns

msf_begindatemsf__c: Fecha de entrada en MSF.
Se puede observar que no hay vacios, se podrá tranformar en "tiempo en MSF" teniendo en cuenta la fecha de las tablas.
Analsis de distribución por variables
-> msf_fechacambiolevelrelacion__c: Variable categorica
In [384]:
# Vamos a realizar analisis por cada variable
var = "msf_fechacambiolevelrelacion__c"
In [385]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_fechacambiolevelrelacion__c es 4. Lo que supone un 0.0008294900295298452%
El nº de vacios para la variable msf_fechacambiolevelrelacion__c es 0. Lo que supone un 0.0%
In [386]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[386]:
# Tot % Tot
2020-03-28 404097 83.799303
2020-07-20 4563 0.946249
2020-09-19 2005 0.415785
2022-01-02 1812 0.375762
2023-01-03 1058 0.219402
2020-09-20 766 0.158849
2021-01-04 604 0.125254
2022-01-15 578 0.119862
2023-01-02 563 0.116752
2022-03-22 412 0.085438
2022-05-06 362 0.075069
2020-09-25 358 0.074240
2022-05-11 346 0.071751
2020-09-22 342 0.070922
2023-02-10 324 0.067189
2022-06-04 315 0.065323
2022-10-21 310 0.064286
2022-12-03 309 0.064079
2020-12-04 301 0.062420
2020-09-23 290 0.060139
2022-11-24 284 0.058894
2023-02-09 272 0.056406
2022-12-23 270 0.055991
2020-09-24 268 0.055576
2022-12-04 257 0.053295
2022-03-05 254 0.052673
2021-06-18 253 0.052466
2023-01-26 252 0.052258
2022-03-11 249 0.051636
2021-02-05 238 0.049355
2023-02-12 236 0.048940
2022-03-12 230 0.047696
2021-03-04 230 0.047696
2023-02-17 226 0.046867
2021-07-08 225 0.046659
2021-03-11 224 0.046452
2023-02-21 222 0.046037
2021-01-03 219 0.045415
2021-12-03 217 0.045000
2022-03-09 215 0.044585
2022-03-10 214 0.044378
2023-02-23 213 0.044171
2022-06-17 212 0.043963
2023-02-15 210 0.043549
2022-02-05 206 0.042719
2023-05-26 206 0.042719
2023-07-05 205 0.042512
2022-12-20 204 0.042304
2023-03-30 203 0.042097
2021-01-28 198 0.041060
2020-11-20 197 0.040853
2023-04-05 196 0.040645
2023-06-16 188 0.038986
2022-11-18 188 0.038986
2022-09-23 185 0.038364
2021-06-25 183 0.037949
2022-12-15 180 0.037327
2022-03-17 180 0.037327
2021-05-20 180 0.037327
2020-11-27 179 0.037120
2023-02-08 176 0.036498
2020-11-06 175 0.036290
2023-05-11 175 0.036290
2021-05-13 175 0.036290
2021-11-18 174 0.036083
2023-06-22 171 0.035461
2023-06-09 168 0.034839
2022-07-07 167 0.034631
2023-06-29 167 0.034631
2022-09-28 166 0.034424
2023-02-16 165 0.034217
2023-06-21 165 0.034217
2022-03-16 165 0.034217
2023-06-30 164 0.034009
2023-07-07 164 0.034009
2021-08-07 164 0.034009
2021-02-11 162 0.033595
2021-01-21 162 0.033595
2022-03-04 161 0.033387
2023-06-28 161 0.033387
2022-09-08 161 0.033387
2022-11-30 160 0.033180
2022-03-15 159 0.032973
2023-07-06 159 0.032973
2022-03-24 158 0.032765
2021-03-18 157 0.032558
2023-05-25 156 0.032350
2022-05-19 156 0.032350
2020-10-03 155 0.032143
2023-05-12 155 0.032143
2022-12-16 155 0.032143
2023-02-24 155 0.032143
2021-05-08 155 0.032143
2021-05-27 155 0.032143
2021-02-18 153 0.031728
2022-10-06 153 0.031728
2022-01-06 152 0.031521
2022-05-12 152 0.031521
2021-03-06 151 0.031314
2023-03-23 151 0.031314
2022-03-08 150 0.031106
2023-02-18 150 0.031106
2020-10-24 150 0.031106
2023-06-15 150 0.031106
2023-06-23 148 0.030691
2022-03-18 148 0.030691
2023-05-24 148 0.030691
2023-01-19 148 0.030691
2023-06-17 148 0.030691
2021-04-29 147 0.030484
2023-03-10 146 0.030277
2023-03-17 146 0.030277
2022-11-10 145 0.030069
2022-11-17 144 0.029862
2023-04-14 144 0.029862
2021-12-22 143 0.029655
2021-04-22 143 0.029655
2021-11-07 142 0.029447
2023-06-01 142 0.029447
2021-04-17 142 0.029447
2023-06-06 142 0.029447
2022-01-28 141 0.029240
2023-03-16 141 0.029240
2022-11-11 140 0.029032
2022-11-23 140 0.029032
2020-10-22 140 0.029032
2021-02-25 140 0.029032
2021-06-10 139 0.028825
2023-04-19 138 0.028618
2021-06-05 138 0.028618
2021-04-01 138 0.028618
2022-11-25 138 0.028618
2023-04-21 138 0.028618
2022-09-30 137 0.028410
2022-12-10 137 0.028410
2023-04-26 137 0.028410
2023-06-08 135 0.027996
2022-10-04 135 0.027996
2023-04-28 134 0.027788
2023-07-08 134 0.027788
2021-04-15 133 0.027581
2023-01-04 132 0.027373
2023-06-24 132 0.027373
2022-03-31 132 0.027373
2023-02-11 132 0.027373
2020-11-17 131 0.027166
2020-10-29 131 0.027166
2022-05-28 131 0.027166
2023-05-18 131 0.027166
2022-03-07 130 0.026959
2022-12-21 130 0.026959
2023-05-31 129 0.026751
2023-02-01 129 0.026751
2021-03-25 128 0.026544
2021-11-13 128 0.026544
2022-11-16 127 0.026337
2022-11-09 127 0.026337
2021-12-23 126 0.026129
2023-01-12 126 0.026129
2023-01-21 125 0.025922
2020-12-25 125 0.025922
2020-12-30 124 0.025714
2023-05-27 124 0.025714
2023-05-09 124 0.025714
2021-10-03 123 0.025507
2021-11-25 123 0.025507
2022-03-25 123 0.025507
2023-03-24 122 0.025300
2023-06-03 122 0.025300
2021-09-09 121 0.025092
2021-10-04 120 0.024885
2023-02-28 120 0.024885
2022-10-20 120 0.024885
2023-03-31 120 0.024885
2022-07-22 119 0.024678
2021-07-29 119 0.024678
2022-10-27 119 0.024678
2022-10-14 118 0.024470
2023-01-06 118 0.024470
2021-06-09 117 0.024263
2023-02-25 117 0.024263
2021-02-10 117 0.024263
2023-04-07 117 0.024263
2021-02-17 116 0.024055
2022-11-26 116 0.024055
2023-06-14 116 0.024055
2021-06-03 116 0.024055
2022-04-28 115 0.023848
2022-02-03 114 0.023641
2023-04-27 114 0.023641
2020-12-14 113 0.023433
2021-07-22 113 0.023433
2022-12-29 113 0.023433
2021-12-17 112 0.023226
2021-10-29 112 0.023226
2021-01-06 112 0.023226
2023-05-19 112 0.023226
2022-11-22 112 0.023226
2022-11-01 111 0.023019
2021-01-14 111 0.023019
2021-09-04 111 0.023019
2020-10-06 111 0.023019
2023-05-07 111 0.023019
2022-06-23 111 0.023019
2023-06-20 110 0.022811
2023-02-07 110 0.022811
2021-11-11 110 0.022811
2021-07-03 110 0.022811
2020-09-29 109 0.022604
2020-12-15 109 0.022604
2021-07-01 109 0.022604
2022-10-28 108 0.022396
2022-04-14 108 0.022396
2023-02-14 108 0.022396
2022-11-08 107 0.022189
2023-01-27 107 0.022189
2022-05-13 107 0.022189
2021-12-14 107 0.022189
2023-02-05 106 0.021982
2022-12-17 106 0.021982
2021-02-24 106 0.021982
2022-06-10 106 0.021982
2023-03-01 106 0.021982
2022-07-14 105 0.021774
2020-10-15 104 0.021567
2023-07-01 104 0.021567
2020-10-20 103 0.021360
2023-05-13 103 0.021360
2022-09-04 103 0.021360
2022-06-29 103 0.021360
2020-12-19 103 0.021360
2022-07-13 102 0.021152
2022-04-09 102 0.021152
2023-04-25 101 0.020945
2023-06-27 101 0.020945
2023-05-30 100 0.020737
2021-09-23 100 0.020737
2022-07-21 99 0.020530
2021-03-27 99 0.020530
2023-05-17 98 0.020323
2023-01-25 98 0.020323
2022-02-10 98 0.020323
2022-07-15 98 0.020323
2022-03-23 98 0.020323
2022-10-26 98 0.020323
2022-09-15 97 0.020115
2022-04-22 97 0.020115
2021-10-16 97 0.020115
2022-12-01 97 0.020115
2021-05-06 96 0.019908
2023-05-16 96 0.019908
2022-04-29 96 0.019908
2022-02-18 96 0.019908
2023-04-22 96 0.019908
2023-01-20 95 0.019701
2021-06-29 95 0.019701
2023-05-23 95 0.019701
2020-12-11 95 0.019701
2023-03-08 94 0.019493
2023-04-20 94 0.019493
2021-07-15 94 0.019493
2021-04-16 94 0.019493
2021-04-08 93 0.019286
2022-05-14 93 0.019286
2022-01-19 93 0.019286
2022-04-08 93 0.019286
2022-05-10 93 0.019286
2020-12-03 93 0.019286
2023-02-03 93 0.019286
2023-03-26 93 0.019286
2022-11-29 92 0.019078
2022-03-03 92 0.019078
2022-11-15 92 0.019078
2021-10-01 91 0.018871
2023-01-31 91 0.018871
2023-05-06 91 0.018871
2021-05-29 91 0.018871
2023-06-13 91 0.018871
2022-01-10 91 0.018871
2023-03-14 91 0.018871
2022-12-14 90 0.018664
2022-02-11 90 0.018664
2022-04-12 90 0.018664
2022-03-26 90 0.018664
2020-10-10 90 0.018664
2021-12-21 90 0.018664
2021-04-23 90 0.018664
2023-03-03 89 0.018456
2021-10-22 89 0.018456
2022-08-06 89 0.018456
2022-05-20 89 0.018456
2021-12-29 89 0.018456
2022-03-30 89 0.018456
2023-01-13 89 0.018456
2021-11-19 89 0.018456
2022-04-01 88 0.018249
2022-11-12 88 0.018249
2023-03-05 88 0.018249
2023-05-20 88 0.018249
2021-10-08 87 0.018042
2022-10-19 87 0.018042
2022-12-30 87 0.018042
2022-06-15 87 0.018042
2020-11-28 86 0.017834
2022-07-10 86 0.017834
2023-01-14 86 0.017834
2020-10-14 86 0.017834
2023-01-28 86 0.017834
2023-03-22 85 0.017627
2022-05-31 85 0.017627
2022-11-19 85 0.017627
2021-12-19 85 0.017627
2021-02-26 85 0.017627
2022-10-25 85 0.017627
2020-10-30 85 0.017627
2022-06-08 85 0.017627
2023-03-21 84 0.017419
2022-02-26 84 0.017419
2022-07-28 84 0.017419
2023-03-18 84 0.017419
2021-11-30 84 0.017419
2022-01-27 84 0.017419
2022-03-19 84 0.017419
2022-09-17 84 0.017419
2022-02-23 83 0.017212
2022-04-07 83 0.017212
2022-03-01 83 0.017212
2021-01-20 83 0.017212
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2022-05-29 2 0.000415
2022-01-04 1 0.000207
2023-07-04 1 0.000207
2023-05-14 1 0.000207
2022-11-14 1 0.000207
2022-06-27 1 0.000207
2021-07-06 1 0.000207
2023-05-21 1 0.000207
2021-05-14 1 0.000207
2021-07-26 1 0.000207
2022-10-13 1 0.000207
2023-06-11 1 0.000207
2022-04-17 1 0.000207
2022-08-28 1 0.000207
2021-08-03 1 0.000207
2022-09-18 1 0.000207
2022-05-02 1 0.000207
2021-09-19 1 0.000207
2022-05-30 1 0.000207
2022-04-24 1 0.000207
2023-04-16 1 0.000207
2022-11-02 1 0.000207
2021-11-08 1 0.000207
2021-05-30 1 0.000207
msf_fechacambiolevelrelacion__c: Fecha de cambio de nivel de relación.
Se puede observar que aunque practicamente no hay vacios ni nulos, el 85% de la misma tiene fecha
Analsis de distribución por variables
-> msf_datefirstdonation__c: Variable fecha
In [387]:
# Vamos a realizar analisis por cada variable
var = "msf_datefirstdonation__c"
In [388]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_datefirstdonation__c es 302093. Lo que supone un 62.64578287268987%
El nº de vacios para la variable msf_datefirstdonation__c es 0. Lo que supone un 0.0%
Out[388]:
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c']
In [389]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[389]:
# Tot % Tot
2017-12-01 3553 1.972453
2010-02-01 3135 1.740400
2020-07-01 2483 1.378441
2014-11-01 2270 1.260194
2003-08-01 1864 1.034802
... ... ...
2012-08-29 1 0.000555
2010-07-28 1 0.000555
2007-07-24 1 0.000555
2012-04-02 1 0.000555
2012-03-05 1 0.000555

8337 rows × 2 columns

msf_datefirstdonation__c: Fecha de la primera donacion.
Se puede observar que hay más de un 62% de los registros a vacio.
Analsis de distribución por variables
-> msf_datefirstrecurringdonorquota__c: Variable fecha
In [390]:
# Vamos a realizar analisis por cada variable
var = "msf_datefirstrecurringdonorquota__c"
In [391]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_datefirstrecurringdonorquota__c es 769. Lo que supone un 0.15946945817711272%
El nº de vacios para la variable msf_datefirstrecurringdonorquota__c es 0. Lo que supone un 0.0%
In [392]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[392]:
# Tot % Tot
2011-01-03 5579 1.158779
2021-01-05 5144 1.068428
2015-01-02 4977 1.033741
2009-01-02 4963 1.030834
2014-11-03 4670 0.969976
2014-12-02 4658 0.967484
2006-01-05 4547 0.944429
2012-01-02 4534 0.941729
2016-07-01 4480 0.930513
2005-02-04 4171 0.866332
2015-10-01 4139 0.859686
2004-02-01 3943 0.818976
2016-01-04 3748 0.778474
2017-04-03 3726 0.773904
2005-01-04 3565 0.740464
2016-08-01 3524 0.731948
2015-11-03 3512 0.729456
2013-01-02 3440 0.714501
2010-01-04 3417 0.709724
2015-12-02 3378 0.701623
2016-04-01 3346 0.694977
2003-03-01 3318 0.689161
2015-02-02 3264 0.677945
2014-05-05 3207 0.666106
2016-10-03 3193 0.663198
2017-06-01 3186 0.661744
2015-03-02 3122 0.648451
2017-01-02 3114 0.646789
2016-12-01 3108 0.645543
2017-12-04 3031 0.629550
2004-01-01 3016 0.626434
2007-01-04 3000 0.623111
2017-07-03 2993 0.621657
2016-05-02 2988 0.620619
2016-11-02 2968 0.616465
2016-06-01 2923 0.607118
2017-03-02 2906 0.603587
2023-07-04 2880 0.598187
2016-03-01 2873 0.596733
2010-02-01 2859 0.593825
2017-02-02 2856 0.593202
2015-04-01 2846 0.591125
2018-01-03 2842 0.590294
2016-02-01 2824 0.586555
2015-05-04 2823 0.586348
2003-01-01 2800 0.581570
2009-02-03 2791 0.579701
2014-01-02 2767 0.574716
2017-08-01 2695 0.559762
2023-06-02 2659 0.552284
2018-02-01 2646 0.549584
2017-05-02 2623 0.544807
2015-06-02 2618 0.543768
2022-04-02 2567 0.533175
2006-02-03 2541 0.527775
2018-03-01 2511 0.521544
2014-06-05 2444 0.507628
2015-08-03 2444 0.507628
2018-06-01 2417 0.502020
2023-03-02 2389 0.496204
2013-02-01 2369 0.492050
2023-04-04 2342 0.486442
2012-02-01 2340 0.486027
2010-12-02 2302 0.478134
2017-09-01 2297 0.477095
2014-08-01 2284 0.474395
2014-04-02 2279 0.473357
2018-07-02 2271 0.471695
2009-12-02 2267 0.470864
2015-07-01 2260 0.469410
2000-02-01 2253 0.467957
2017-11-02 2247 0.466710
2018-12-03 2247 0.466710
2014-10-02 2220 0.461102
2019-01-02 2210 0.459025
2020-02-03 2208 0.458610
2022-12-02 2197 0.456325
2014-02-03 2192 0.455287
2018-08-01 2185 0.453833
2017-10-02 2145 0.445525
2020-01-02 2123 0.440955
2018-11-02 2108 0.437839
2011-12-01 2093 0.434724
2007-02-05 2083 0.432647
2022-07-05 2079 0.431816
2023-02-02 2076 0.431193
2019-12-02 2070 0.429947
2011-02-01 2060 0.427870
2016-09-01 2034 0.422469
2018-04-03 2033 0.422262
2005-03-04 1978 0.410838
2019-11-04 1962 0.407515
2022-11-03 1946 0.404191
2014-07-02 1941 0.403153
2020-03-02 1939 0.402738
2019-05-02 1936 0.402114
2019-06-03 1913 0.397337
2019-02-01 1909 0.396506
2013-05-02 1869 0.388198
2019-07-01 1859 0.386121
2018-05-03 1855 0.385290
2013-12-02 1842 0.382590
2021-03-02 1840 0.382175
2000-01-01 1833 0.380721
2014-03-03 1829 0.379890
2019-08-01 1828 0.379682
1994-10-01 1820 0.378021
2001-03-01 1815 0.376982
2008-12-01 1800 0.373867
2019-04-01 1799 0.373659
2023-01-03 1787 0.371167
2023-05-03 1783 0.370336
2022-06-02 1775 0.368674
2022-10-04 1774 0.368466
2021-04-02 1774 0.368466
2013-11-04 1767 0.367012
2021-07-02 1746 0.362651
2018-10-02 1728 0.358912
2020-05-03 1727 0.358704
2022-08-02 1716 0.356420
2019-10-02 1708 0.354758
2013-08-02 1707 0.354550
2014-09-03 1701 0.353304
2019-03-01 1699 0.352889
2013-06-03 1687 0.350396
1995-02-01 1677 0.348319
2021-06-02 1664 0.345619
2008-02-04 1642 0.341050
2022-01-04 1627 0.337934
2013-04-02 1596 0.331495
2004-03-01 1581 0.328380
2012-12-03 1573 0.326718
2021-12-02 1572 0.326510
2015-09-01 1543 0.320487
2021-05-04 1542 0.320279
2021-02-02 1540 0.319864
2022-05-03 1534 0.318618
1998-03-01 1533 0.318410
2021-10-02 1526 0.316956
2011-04-01 1525 0.316748
2011-08-02 1517 0.315087
2001-02-01 1515 0.314671
2022-03-02 1502 0.311971
2020-04-02 1499 0.311348
2022-02-02 1497 0.310932
2018-09-03 1495 0.310517
2013-03-01 1493 0.310102
2013-07-01 1477 0.306778
2012-11-02 1475 0.306363
2011-03-01 1472 0.305740
2021-11-03 1463 0.303871
2008-01-03 1443 0.299716
2006-12-02 1393 0.289331
1994-02-01 1360 0.282477
1999-01-01 1331 0.276454
2021-08-03 1316 0.273338
2010-03-01 1313 0.272715
2013-10-02 1306 0.271261
2002-01-01 1299 0.269807
2012-08-01 1293 0.268561
2009-03-03 1292 0.268353
2010-08-02 1283 0.266484
2011-11-02 1259 0.261499
2013-09-02 1240 0.257553
2012-03-01 1235 0.256514
2007-12-02 1226 0.254645
2012-04-02 1226 0.254645
2012-06-04 1225 0.254437
2011-09-02 1205 0.250283
2020-06-02 1188 0.246752
2020-08-03 1166 0.242183
2012-07-02 1164 0.241767
1999-02-01 1144 0.237613
2019-09-02 1107 0.229928
2005-12-03 1104 0.229305
2011-05-02 1062 0.220581
2011-07-01 1062 0.220581
1996-02-01 1050 0.218089
2010-04-01 1033 0.214558
2011-10-04 1031 0.214143
2001-01-01 1022 0.212273
2012-05-03 995 0.206665
2008-08-08 988 0.205211
2010-07-01 985 0.204588
2006-11-03 959 0.199188
2020-07-01 959 0.199188
2012-10-01 957 0.198772
2011-06-01 956 0.198565
1994-07-01 931 0.193372
2006-03-03 921 0.191295
2007-04-02 920 0.191087
2009-07-02 912 0.189426
1997-02-01 905 0.187972
2009-04-02 895 0.185895
2007-03-02 889 0.184649
2022-09-02 881 0.182987
2008-04-04 878 0.182364
2002-12-01 877 0.182156
2008-03-03 864 0.179456
2010-06-02 859 0.178418
1998-02-01 815 0.169279
2021-09-02 814 0.169071
2007-05-04 795 0.165124
2010-10-04 792 0.164501
2005-11-03 774 0.160763
2008-06-02 773 0.160555
2006-04-03 772 0.160347
1995-04-01 763 0.158478
2007-07-04 757 0.157232
2010-05-03 749 0.155570
2008-07-04 746 0.154947
2005-08-02 741 0.153908
2004-12-05 735 0.152662
2006-06-02 735 0.152662
2009-06-04 713 0.148093
2009-05-04 706 0.146639
2009-08-03 705 0.146431
2006-07-03 698 0.144977
1994-01-01 691 0.143523
2007-10-04 684 0.142069
2005-07-04 679 0.141031
2003-12-01 675 0.140200
2000-03-01 674 0.139992
2009-10-02 673 0.139785
2007-09-03 672 0.139577
2010-11-02 672 0.139577
2007-08-02 658 0.136669
2012-09-03 657 0.136461
1992-11-01 647 0.134384
2005-06-03 637 0.132307
2020-09-01 625 0.129815
2009-11-02 621 0.128984
1995-03-01 615 0.127738
2007-06-05 597 0.123999
2008-05-02 596 0.123791
2009-09-02 585 0.121507
2008-09-01 582 0.120884
1994-09-01 581 0.120676
2010-09-02 570 0.118391
1998-01-01 556 0.115483
2005-09-02 542 0.112575
2005-04-04 531 0.110291
2008-10-02 522 0.108421
2008-11-03 513 0.106552
1999-06-01 511 0.106137
2007-11-02 505 0.104890
2002-04-01 494 0.102606
2005-05-04 487 0.101152
1997-01-01 480 0.099698
1994-03-01 480 0.099698
1999-03-01 475 0.098659
2003-06-01 458 0.095128
2004-04-01 432 0.089728
2005-10-03 425 0.088274
2002-02-01 425 0.088274
1995-07-01 417 0.086612
2003-04-01 416 0.086405
2006-08-02 408 0.084743
1995-01-01 394 0.081835
2002-05-01 391 0.081212
2003-08-01 383 0.079551
2000-05-01 382 0.079343
2001-04-01 379 0.078720
2006-05-04 372 0.077266
1994-04-01 361 0.074981
1999-07-01 353 0.073319
2006-09-04 339 0.070412
2004-11-04 337 0.069996
2006-10-02 337 0.069996
2001-08-01 332 0.068958
2003-11-01 329 0.068335
2004-06-01 325 0.067504
1995-10-01 325 0.067504
1992-12-01 323 0.067088
2000-04-01 318 0.066050
2004-05-01 317 0.065842
1999-12-01 302 0.062727
1996-04-01 295 0.061273
1998-04-01 293 0.060857
2003-05-01 289 0.060026
2001-12-01 289 0.060026
1996-12-01 273 0.056703
1998-12-01 273 0.056703
2000-01-13 271 0.056288
1999-05-01 269 0.055872
2001-07-01 259 0.053795
2004-08-01 258 0.053588
1996-03-01 257 0.053380
2002-08-01 247 0.051303
2002-11-01 245 0.050887
1996-01-01 243 0.050472
1994-06-01 242 0.050264
1996-06-01 240 0.049849
1998-11-01 239 0.049641
1998-09-01 232 0.048187
2004-10-06 232 0.048187
1995-06-01 230 0.047772
2002-03-01 224 0.046526
1998-05-01 220 0.045695
1992-06-01 217 0.045072
1993-01-01 215 0.044656
1997-03-01 212 0.044033
2004-07-01 212 0.044033
1993-11-01 211 0.043825
2000-06-01 206 0.042787
2003-10-01 204 0.042372
1993-03-01 194 0.040295
1998-06-01 193 0.040087
1993-07-01 189 0.039256
2003-09-01 186 0.038633
1994-08-01 186 0.038633
1996-07-01 177 0.036764
2020-12-02 175 0.036348
1999-04-01 175 0.036348
2002-09-01 172 0.035725
2003-07-01 164 0.034063
1994-05-01 163 0.033856
1994-12-01 163 0.033856
2000-07-01 159 0.033025
1997-11-01 157 0.032609
2002-10-01 156 0.032402
2004-09-03 154 0.031986
1995-05-01 153 0.031779
1993-02-01 151 0.031363
2003-02-01 149 0.030948
2001-11-01 145 0.030117
1999-08-01 135 0.028040
1993-12-01 135 0.028040
1995-12-01 123 0.025548
1995-11-01 123 0.025548
1995-09-01 115 0.023886
1994-11-01 115 0.023886
1994-01-11 115 0.023886
2001-05-01 112 0.023263
1996-05-01 111 0.023055
1997-06-01 111 0.023055
1998-07-01 110 0.022847
1997-12-01 105 0.021809
1998-08-01 104 0.021601
2020-10-02 101 0.020978
1996-08-01 94 0.019524
2001-09-01 93 0.019316
2000-04-05 92 0.019109
1997-05-01 91 0.018901
1992-07-01 90 0.018693
2001-10-01 89 0.018486
1993-10-01 87 0.018070
2000-08-01 84 0.017447
2000-12-01 84 0.017447
1997-07-01 83 0.017239
1996-09-01 82 0.017032
1999-11-01 81 0.016824
1993-06-01 79 0.016409
1999-09-01 77 0.015993
1997-04-01 75 0.015578
1996-10-01 73 0.015162
1993-05-01 73 0.015162
2001-06-01 70 0.014539
1992-08-01 68 0.014124
1999-10-01 68 0.014124
2000-11-01 67 0.013916
1996-11-01 65 0.013501
2002-06-17 64 0.013293
2000-03-09 63 0.013085
2000-10-01 63 0.013085
1995-08-01 63 0.013085
2020-11-04 62 0.012878
2000-09-01 60 0.012462
1997-08-01 60 0.012462
1993-04-01 57 0.011839
1997-09-01 56 0.011631
2002-06-13 53 0.011008
1992-10-01 51 0.010593
2002-06-07 50 0.010385
1998-10-01 49 0.010177
1992-09-01 47 0.009762
2002-06-12 46 0.009554
1993-08-01 43 0.008931
1997-10-01 41 0.008516
2002-06-11 39 0.008100
1993-09-01 39 0.008100
1991-01-20 37 0.007685
2021-02-05 36 0.007477
2002-06-19 35 0.007270
2015-02-01 34 0.007062
2002-06-14 33 0.006854
1991-02-20 33 0.006854
2015-03-01 32 0.006647
2002-06-06 32 0.006647
2002-07-05 29 0.006023
2017-01-01 27 0.005608
1992-01-02 26 0.005400
2002-06-10 26 0.005400
2015-01-01 26 0.005400
2015-06-01 25 0.005193
2015-11-01 25 0.005193
2014-12-01 24 0.004985
2017-07-01 24 0.004985
1991-08-01 23 0.004777
2016-01-01 22 0.004569
1991-12-01 21 0.004362
2017-10-01 20 0.004154
2016-11-01 20 0.004154
2014-07-01 19 0.003946
2015-12-01 19 0.003946
2017-04-01 19 0.003946
2015-05-01 19 0.003946
2016-05-01 18 0.003739
2002-07-04 18 0.003739
1991-11-15 18 0.003739
2014-01-01 18 0.003739
2013-11-01 17 0.003531
2017-11-01 17 0.003531
2017-03-01 17 0.003531
2014-05-01 17 0.003531
2017-02-01 16 0.003323
2017-05-01 16 0.003323
2014-09-01 15 0.003116
1991-11-01 15 0.003116
2020-02-01 15 0.003116
2014-03-01 15 0.003116
2002-06-28 15 0.003116
2015-08-01 15 0.003116
1991-07-01 15 0.003116
2013-09-01 15 0.003116
1991-11-06 15 0.003116
2019-01-01 14 0.002908
2013-12-01 14 0.002908
1992-03-02 14 0.002908
2012-01-01 14 0.002908
2002-06-21 14 0.002908
2014-11-01 13 0.002700
2002-06-18 13 0.002700
2012-04-01 13 0.002700
1992-06-02 13 0.002700
1991-10-01 13 0.002700
2012-07-01 13 0.002700
2020-01-01 13 0.002700
2012-06-01 12 0.002492
1992-02-02 12 0.002492
1991-01-21 12 0.002492
2018-01-01 12 0.002492
2018-09-01 12 0.002492
2013-08-01 12 0.002492
2012-09-01 11 0.002285
1991-06-03 11 0.002285
2018-07-01 11 0.002285
2009-02-01 11 0.002285
2020-04-01 11 0.002285
2018-05-01 11 0.002285
1991-03-25 11 0.002285
2014-02-01 11 0.002285
2014-10-01 11 0.002285
2018-12-01 11 0.002285
2009-03-01 11 0.002285
2010-09-01 10 0.002077
1991-11-11 10 0.002077
2010-12-01 10 0.002077
2011-05-01 10 0.002077
2017-12-01 10 0.002077
1994-10-06 10 0.002077
2013-04-01 10 0.002077
2018-04-01 10 0.002077
2019-06-01 10 0.002077
2016-10-01 10 0.002077
2020-03-01 10 0.002077
2014-04-01 10 0.002077
2007-03-01 10 0.002077
2018-11-01 9 0.001869
2019-11-01 9 0.001869
2019-12-01 9 0.001869
2008-02-01 9 0.001869
2010-01-01 9 0.001869
2008-03-01 9 0.001869
2013-05-01 9 0.001869
2014-06-01 8 0.001662
1992-01-14 8 0.001662
2019-05-01 8 0.001662
2020-05-01 8 0.001662
2012-11-01 8 0.001662
2013-06-01 8 0.001662
1994-03-28 8 0.001662
2002-07-03 8 0.001662
2013-10-01 8 0.001662
2019-09-01 7 0.001454
2009-08-01 7 0.001454
1991-09-01 7 0.001454
2018-10-01 7 0.001454
2011-01-01 7 0.001454
2010-08-01 7 0.001454
2011-09-01 7 0.001454
2012-05-01 7 0.001454
2019-10-01 7 0.001454
2011-11-01 7 0.001454
2006-05-01 6 0.001246
2009-09-01 6 0.001246
2008-06-01 6 0.001246
2011-08-01 6 0.001246
2007-02-01 6 0.001246
2006-12-01 6 0.001246
2008-10-01 5 0.001039
2013-01-01 5 0.001039
2008-04-01 5 0.001039
2008-08-01 5 0.001039
2007-08-01 5 0.001039
2009-12-01 5 0.001039
1995-10-02 5 0.001039
2012-12-01 5 0.001039
2007-01-01 5 0.001039
1992-02-01 5 0.001039
2008-05-01 5 0.001039
1993-11-08 5 0.001039
1991-05-16 4 0.000831
2010-05-01 4 0.000831
2009-06-01 4 0.000831
2020-06-01 4 0.000831
2007-04-01 4 0.000831
2007-10-01 4 0.000831
1996-01-02 4 0.000831
1992-12-14 4 0.000831
2011-10-01 4 0.000831
1993-12-07 4 0.000831
2010-11-01 4 0.000831
1995-01-02 4 0.000831
2009-11-01 4 0.000831
1993-10-04 3 0.000623
1994-03-04 3 0.000623
1992-04-01 3 0.000623
2007-11-01 3 0.000623
2006-10-01 3 0.000623
2010-10-01 3 0.000623
1993-04-05 3 0.000623
1992-09-25 3 0.000623
2008-11-01 3 0.000623
2006-04-01 3 0.000623
2002-06-26 3 0.000623
2002-06-05 3 0.000623
1994-07-04 3 0.000623
1992-01-16 3 0.000623
2005-12-01 3 0.000623
1995-05-02 2 0.000415
1990-12-10 2 0.000415
2009-10-01 2 0.000415
2010-06-01 2 0.000415
2006-06-01 2 0.000415
1994-01-07 2 0.000415
2008-07-01 2 0.000415
1996-09-02 2 0.000415
2002-07-01 2 0.000415
2008-01-01 2 0.000415
2002-06-25 2 0.000415
2006-07-01 2 0.000415
2007-05-01 2 0.000415
1993-09-16 2 0.000415
2002-07-02 2 0.000415
2006-08-01 2 0.000415
1995-09-07 2 0.000415
1993-02-09 2 0.000415
2007-07-01 2 0.000415
2005-09-01 2 0.000415
2009-01-01 2 0.000415
1993-03-11 2 0.000415
2007-12-01 2 0.000415
2007-06-01 2 0.000415
2005-10-01 2 0.000415
1993-01-07 2 0.000415
1991-10-25 1 0.000208
1992-09-29 1 0.000208
1994-02-07 1 0.000208
1992-04-02 1 0.000208
1995-07-19 1 0.000208
1990-02-20 1 0.000208
2002-07-27 1 0.000208
1990-03-31 1 0.000208
1995-10-26 1 0.000208
1994-06-06 1 0.000208
1992-03-24 1 0.000208
1991-01-08 1 0.000208
2000-03-30 1 0.000208
1996-12-26 1 0.000208
1991-01-10 1 0.000208
1994-10-10 1 0.000208
1991-10-21 1 0.000208
2002-07-19 1 0.000208
1994-01-10 1 0.000208
1991-04-02 1 0.000208
1996-12-28 1 0.000208
1999-12-28 1 0.000208
1992-05-15 1 0.000208
1990-04-20 1 0.000208
2002-07-17 1 0.000208
1992-05-22 1 0.000208
2020-08-01 1 0.000208
2009-05-01 1 0.000208
1990-02-12 1 0.000208
1993-04-12 1 0.000208
1993-02-04 1 0.000208
1993-02-05 1 0.000208
1999-03-25 1 0.000208
1993-08-05 1 0.000208
1994-10-11 1 0.000208
1993-04-30 1 0.000208
1994-08-19 1 0.000208
1995-10-11 1 0.000208
1991-12-03 1 0.000208
1993-03-16 1 0.000208
1996-09-25 1 0.000208
2006-09-01 1 0.000208
2006-11-01 1 0.000208
1996-12-20 1 0.000208
2005-11-01 1 0.000208
1995-10-21 1 0.000208
2002-07-28 1 0.000208
1993-02-08 1 0.000208
1991-05-08 1 0.000208
1992-09-23 1 0.000208
1993-02-17 1 0.000208
1996-12-21 1 0.000208
2009-04-01 1 0.000208
1998-08-18 1 0.000208
2002-05-28 1 0.000208
1994-12-04 1 0.000208
1996-12-13 1 0.000208
1997-04-02 1 0.000208
1990-06-10 1 0.000208
1991-04-23 1 0.000208
1992-03-01 1 0.000208
1991-03-20 1 0.000208
1996-09-30 1 0.000208
1990-03-01 1 0.000208
1998-10-20 1 0.000208
1991-06-01 1 0.000208
msf_datefirstrecurringdonorquota__c: Fecha de la primera donacion recurrente.
Se puede observar que apenas hay vacios. Ya se tiene esta información muy parecida en otras variables por lo que se descarta.
Analsis de distribución por variables
-> msf_datelastrecurringdonorquota__c: Variable fecha
In [393]:
# Vamos a realizar analisis por cada variable
var = "msf_datelastrecurringdonorquota__c"
In [394]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_datelastrecurringdonorquota__c es 769. Lo que supone un 0.15946945817711272%
El nº de vacios para la variable msf_datelastrecurringdonorquota__c es 0. Lo que supone un 0.0%
In [395]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[395]:
# Tot % Tot
2023-07-04 391625 81.341974
2023-06-02 18460 3.834211
2023-05-03 17319 3.597221
2023-02-02 9792 2.033835
2023-01-03 9783 2.031966
2023-03-02 7026 1.459326
2022-12-02 6630 1.377076
2023-04-04 5758 1.195958
2022-11-03 4380 0.909742
2022-08-02 4104 0.852416
2022-10-04 3661 0.760403
2022-09-02 2687 0.558100
2022-07-05 10 0.002077
2020-09-01 9 0.001869
2018-01-03 7 0.001454
2023-07-03 6 0.001246
2019-02-01 6 0.001246
2018-12-03 5 0.001039
2022-06-02 5 0.001039
2020-02-03 5 0.001039
2020-01-02 5 0.001039
2019-11-04 4 0.000831
2020-07-01 4 0.000831
2018-04-03 4 0.000831
2019-08-01 4 0.000831
2022-01-04 3 0.000623
2018-06-01 3 0.000623
2017-07-03 3 0.000623
2020-03-02 3 0.000623
2020-04-02 3 0.000623
2022-02-02 3 0.000623
2017-10-02 3 0.000623
2020-10-02 3 0.000623
2015-09-01 3 0.000623
2020-11-04 3 0.000623
2018-07-02 3 0.000623
2014-01-02 3 0.000623
2018-08-01 3 0.000623
2015-11-03 3 0.000623
2015-05-04 2 0.000415
2016-11-02 2 0.000415
2017-12-04 2 0.000415
2020-06-02 2 0.000415
2018-09-03 2 0.000415
2023-06-01 2 0.000415
2017-01-02 2 0.000415
2022-05-03 2 0.000415
2020-05-03 2 0.000415
2017-02-02 2 0.000415
2014-05-05 2 0.000415
2019-01-02 2 0.000415
2016-03-01 2 0.000415
2013-06-03 2 0.000415
2014-03-03 2 0.000415
2012-12-03 2 0.000415
2016-02-01 2 0.000415
2016-10-03 2 0.000415
2017-08-01 2 0.000415
2016-09-01 2 0.000415
2017-04-03 2 0.000415
2019-04-01 2 0.000415
2018-10-02 2 0.000415
2017-05-02 2 0.000415
2013-10-02 2 0.000415
2020-12-02 1 0.000208
2006-04-03 1 0.000208
2021-10-02 1 0.000208
2013-12-02 1 0.000208
2019-10-02 1 0.000208
2013-08-02 1 0.000208
2019-12-02 1 0.000208
2014-02-03 1 0.000208
2016-08-01 1 0.000208
2018-05-01 1 0.000208
2012-09-03 1 0.000208
2018-11-02 1 0.000208
2016-05-02 1 0.000208
2010-07-01 1 0.000208
2014-06-05 1 0.000208
2011-01-03 1 0.000208
2014-07-02 1 0.000208
2021-02-02 1 0.000208
2021-12-02 1 0.000208
2015-05-01 1 0.000208
2007-08-02 1 0.000208
2016-06-01 1 0.000208
2009-04-02 1 0.000208
2012-05-03 1 0.000208
2021-09-02 1 0.000208
2019-07-01 1 0.000208
2016-04-01 1 0.000208
2019-05-02 1 0.000208
2011-08-02 1 0.000208
2015-12-02 1 0.000208
2010-05-03 1 0.000208
2018-05-03 1 0.000208
2022-12-01 1 0.000208
2018-09-01 1 0.000208
2017-11-02 1 0.000208
2021-01-05 1 0.000208
2009-10-02 1 0.000208
2019-06-03 1 0.000208
2021-11-03 1 0.000208
2021-06-02 1 0.000208
2017-09-01 1 0.000208
2018-03-01 1 0.000208
2010-01-04 1 0.000208
2009-02-03 1 0.000208
2012-03-01 1 0.000208
2013-04-02 1 0.000208
2016-07-01 1 0.000208
2009-09-02 1 0.000208
2013-03-01 1 0.000208
2010-03-01 1 0.000208
2015-03-02 1 0.000208
2021-07-02 1 0.000208
2021-08-03 1 0.000208
2018-02-01 1 0.000208
2015-02-02 1 0.000208
2010-06-02 1 0.000208
2016-12-01 1 0.000208
2006-06-02 1 0.000208
2010-08-02 1 0.000208
2011-03-01 1 0.000208
2013-01-02 1 0.000208
2015-10-01 1 0.000208
2019-03-01 1 0.000208
2011-10-04 1 0.000208
msf_datelastrecurringdonorquota__c: Fecha de la ultima donacion recurrente.
Se puede observar que hay un 0,15% de vacios. Se va a priorizar la de inicio de la donacion recurrente en esta misma tabla.
Analsis de distribución por variables
-> msf_datelastdonation__c: Variable fecha
In [396]:
# Vamos a realizar analisis por cada variable
var = "msf_datelastdonation__c"
In [397]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_datelastdonation__c es 301052. Lo que supone un 62.429908092504725%
El nº de vacios para la variable msf_datelastdonation__c es 0. Lo que supone un 0.0%
Out[397]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c']
In [398]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[398]:
# Tot % Tot
2022-12-02 6165 3.402844
2023-03-02 5664 3.126311
2020-07-01 4689 2.588148
2023-07-04 3146 1.736471
2021-12-02 3056 1.686795
... ... ...
1995-01-27 1 0.000552
2005-07-07 1 0.000552
2010-03-07 1 0.000552
2016-08-17 1 0.000552
2009-07-16 1 0.000552

6537 rows × 2 columns

msf_datelastdonation__c: Fecha de la ultima donacion.
Se puede observar que 65% de vacios. Al igual que no se tiene en cuenta la de la primera, tampoco la de la ultima.
Analsis de distribución por variables
-> npsp__largest_soft_credit_date__c: Variable fecha
In [399]:
# Vamos a realizar analisis por cada variable
var = "npsp__largest_soft_credit_date__c"
In [400]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npsp__largest_soft_credit_date__c es 482224. Lo que supone un 100.0%
El nº de vacios para la variable npsp__largest_soft_credit_date__c es 0. Lo que supone un 0.0%
Out[400]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c']
In [401]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[401]:
# Tot % Tot
npsp__largest_soft_credit_date__c: Fecha de la aportacion indirecta más importante.
Se puede observar que tiene un 100% de vacios.
Analsis de distribución por variables
-> npsp__first_soft_credit_date__c: Variable fecha
In [402]:
# Vamos a realizar analisis por cada variable
var = "npsp__first_soft_credit_date__c"
In [403]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npsp__first_soft_credit_date__c es 482224. Lo que supone un 100.0%
El nº de vacios para la variable npsp__first_soft_credit_date__c es 0. Lo que supone un 0.0%
Out[403]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c']
In [404]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[404]:
# Tot % Tot
npsp__first_soft_credit_date__c: Fecha de la primera aportación indirecta.
Se puede observar que 100% vacios.
Analsis de distribución por variables
-> msf_entrydatecurrentrecurringdonor__c: Variable fecha
In [405]:
# Vamos a realizar analisis por cada variable
var = "msf_entrydatecurrentrecurringdonor__c"
In [406]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_entrydatecurrentrecurringdonor__c es 1. Lo que supone un 0.0002073725073824613%
El nº de vacios para la variable msf_entrydatecurrentrecurringdonor__c es 0. Lo que supone un 0.0%
In [407]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[407]:
# Tot % Tot
2000-02-01 1895 0.392972
2000-01-01 1558 0.323087
1994-10-01 1513 0.313755
1995-02-01 1431 0.296751
2004-01-01 1350 0.279953
... ... ...
2008-04-19 1 0.000207
2009-07-11 1 0.000207
2002-08-30 1 0.000207
1996-09-25 1 0.000207
2014-05-10 1 0.000207

7586 rows × 2 columns

msf_entrydatecurrentrecurringdonor__c: Fecha de la ultima entrada de socio.
Se puede observar que tiene menos del 1% de registros a vacio. Es una variable bastanteen esta misma tabla, por lo que se descarta.
Analsis de distribución por variables
-> npsp__last_soft_credit_date__c: Variable fecha
In [408]:
# Vamos a realizar analisis por cada variable
var = "npsp__last_soft_credit_date__c"
In [409]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npsp__last_soft_credit_date__c es 482224. Lo que supone un 100.0%
El nº de vacios para la variable npsp__last_soft_credit_date__c es 0. Lo que supone un 0.0%
Out[409]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c']
In [410]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[410]:
# Tot % Tot
npsp__last_soft_credit_date__c: Fecha de la ultima aportación indirecta.
Se puede observar que tiene todos los registros como vacios.
Analsis de distribución por variables
-> msf_firstentrydaterecurringdonor__c: Variable fecha
In [411]:
# Vamos a realizar analisis por cada variable
var = "msf_firstentrydaterecurringdonor__c"
In [412]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_firstentrydaterecurringdonor__c es 1. Lo que supone un 0.0002073725073824613%
El nº de vacios para la variable msf_firstentrydaterecurringdonor__c es 0. Lo que supone un 0.0%
In [413]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[413]:
# Tot % Tot
2000-02-01 2282 0.473225
2000-01-01 1854 0.384469
1994-10-01 1851 0.383847
2004-01-01 1834 0.380322
1995-02-01 1698 0.352119
... ... ...
2008-04-12 1 0.000207
1996-11-27 1 0.000207
1991-04-02 1 0.000207
2002-08-02 1 0.000207
2011-06-25 1 0.000207

7662 rows × 2 columns

msf_firstentrydaterecurringdonor__c: Fecha de la primera entrada de socio.
Se puede observar que casi no hay registros a vacio. Se utilizará para diferencia de fechas.
Analsis de distribución por variables
-> npo02__firstclosedate__c: Variable fecha
In [414]:
# Vamos a realizar analisis por cada variable
var = "npo02__firstclosedate__c"
In [415]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__firstclosedate__c es 825. Lo que supone un 0.17108231859053055%
El nº de vacios para la variable npo02__firstclosedate__c es 0. Lo que supone un 0.0%
In [416]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[416]:
# Tot % Tot
2016-12-01 29229 6.071679
2015-12-02 27769 5.768396
2017-12-04 26675 5.541142
2014-12-02 24859 5.163908
2013-12-02 15927 3.308482
... ... ...
2000-03-24 1 0.000208
1996-06-17 1 0.000208
2015-02-16 1 0.000208
2009-01-16 1 0.000208
2012-03-05 1 0.000208

6745 rows × 2 columns

npo02__firstclosedate__c: Fecha de la primera donación de cualquier tipo.
Se puede observar que hay un 5% de vacios.
Analsis de distribución por variables
-> msf_lastrecurringdonationdate__c: Variable fecha
In [417]:
# Vamos a realizar analisis por cada variable
var = "msf_lastrecurringdonationdate__c"
In [418]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_lastrecurringdonationdate__c es 420974. Lo que supone un 87.29843392282424%
El nº de vacios para la variable msf_lastrecurringdonationdate__c es 0. Lo que supone un 0.0%
Out[418]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c']
In [419]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[419]:
# Tot % Tot
2014-03-13 842 1.374694
2016-02-04 372 0.607347
2020-03-12 304 0.496327
2020-09-01 248 0.404898
2014-02-07 228 0.372245
... ... ...
2004-01-07 1 0.001633
2010-03-02 1 0.001633
2016-06-16 1 0.001633
2003-06-27 1 0.001633
2019-11-01 1 0.001633

5090 rows × 2 columns

msf_lastrecurringdonationdate__c: Fecha de la ultima baja de socio.
Se puede observar que existen un 42% de vacios. Lo que puede cuadrar con la informacion de donantes recurrentes activos de la tabla de donaciones recurrentes.
Analsis de distribución por variables
-> npo02__lastclosedate__c: Variable fecha
In [420]:
# Vamos a realizar analisis por cada variable
var = "npo02__lastclosedate__c"
In [421]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__lastclosedate__c es 482224. Lo que supone un 100.0%
El nº de vacios para la variable npo02__lastclosedate__c es 0. Lo que supone un 0.0%
Out[421]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c']
In [422]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[422]:
# Tot % Tot
npo02__lastclosedate__c: Fecha de la ultima donación de cualquier tipo.
Se puede observar que tdos los registros están a vacios.
Analsis de distribución por variables
-> gender__c: Variable categorica
In [423]:
# Vamos a realizar analisis por cada variable
var = "gender__c"
In [424]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable gender__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable gender__c es 8953. Lo que supone un 1.8566060585951758%
In [425]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[425]:
# Tot % Tot
Female 271434 56.287949
Male 199744 41.421414
8953 1.856606
Other 2091 0.433616
H 1 0.000207
M 1 0.000207
gender__c: Genero.
Se puede observar que existe un 1% de registros a vacio. Se incluirá como variable candidata.
Analsis de distribución por variables
-> msf_languagepreferer__c: Variable categorica
In [426]:
# Vamos a realizar analisis por cada variable
var = "msf_languagepreferer__c"
In [427]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_languagepreferer__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_languagepreferer__c es 0. Lo que supone un 0.0%
In [428]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[428]:
# Tot % Tot
ESP 416856 86.444474
CAT 56510 11.718620
GAL 5613 1.163982
EUS 3238 0.671472
ING 7 0.001452
msf_languagepreferer__c: lenguaje de comunicacion.
Se puede observar que no tiene vacios, casi todos los casos son Español, y en menor medida Catalan.
Analsis de distribución por variables
-> npo02__largestamount__c: Variable numerica
In [429]:
# Vamos a realizar analisis por cada variable
var = "npo02__largestamount__c"
In [430]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__largestamount__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__largestamount__c es 0. Lo que supone un 0.0%
In [431]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[431]:
# Tot % Tot
0.0 482224 100.0
npo02__largestamount__c: importe de la donacion más grande.
Se puede observar que aunque no tenga vacios, siempre se informa a 0.
Analsis de distribución por variables
-> npo02__smallestamount__c: Variable numerica
In [432]:
# Vamos a realizar analisis por cada variable
var = "npo02__smallestamount__c"
In [433]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__smallestamount__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__smallestamount__c es 0. Lo que supone un 0.0%
In [434]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[434]:
# Tot % Tot
0.0 482224 100.0
npo02__smallestamount__c: importe de la donacion más pequeña.
Se puede observar que aunque no tenga vacios, la variable siempre está informada a 0.
Analsis de distribución por variables
-> npsp__first_soft_credit_amount__c: Variable numerica
In [435]:
# Vamos a realizar analisis por cada variable
var = "npsp__first_soft_credit_amount__c"
In [436]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npsp__first_soft_credit_amount__c es 482224. Lo que supone un 100.0%
El nº de vacios para la variable npsp__first_soft_credit_amount__c es 0. Lo que supone un 0.0%
Out[436]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c']
In [437]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[437]:
# Tot % Tot
npsp__first_soft_credit_amount__c: importe de la primera aportacion indirecta.
Se puede observar que esta variable es nula en su totalidad.
Analsis de distribución por variables
-> npo02__lastoppamount__c: Variable numerica
In [438]:
# Vamos a realizar analisis por cada variable
var = "npo02__lastoppamount__c"
In [439]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__lastoppamount__c es 107. Lo que supone un 0.022188858289923355%
El nº de vacios para la variable npo02__lastoppamount__c es 0. Lo que supone un 0.0%
In [440]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[440]:
# Tot % Tot
10.00 79772 16.546191
15.00 54933 11.394122
20.00 42410 8.796620
5.00 29168 6.049984
12.00 26997 5.599678
30.00 25561 5.301825
25.00 17083 3.543331
6.00 14790 3.067720
50.00 13703 2.842256
60.00 10554 2.189095
14.00 10412 2.159642
8.00 9950 2.063814
100.00 9378 1.945171
7.00 8906 1.847269
3.00 8838 1.833165
40.00 8616 1.787118
17.00 6080 1.261105
35.00 5612 1.164033
18.00 5262 1.091436
150.00 4300 0.891900
16.00 4215 0.874269
9.00 3910 0.811006
13.00 3725 0.772634
22.00 3686 0.764545
26.00 3419 0.709164
120.00 3412 0.707712
11.00 3008 0.623915
19.00 2920 0.605662
45.00 2876 0.596536
200.00 2785 0.577661
70.00 2472 0.512739
23.00 2277 0.472292
75.00 2234 0.463373
30.05 2230 0.462543
80.00 2142 0.444290
6.01 1972 0.409029
90.00 1951 0.404674
36.00 1603 0.332492
60.10 1524 0.316106
32.00 1502 0.311543
24.00 1496 0.310298
4.33 1486 0.308224
4.00 1379 0.286030
18.03 1340 0.277941
300.00 1336 0.277111
28.00 1203 0.249524
21.00 1161 0.240813
65.00 1142 0.236872
250.00 1107 0.229612
55.00 1092 0.226501
27.00 1090 0.226086
42.00 954 0.197877
33.00 905 0.187714
125.00 744 0.154319
0.00 718 0.148927
130.00 702 0.145608
12.02 695 0.144156
180.00 677 0.140422
110.00 641 0.132955
140.00 558 0.115740
72.00 551 0.114288
90.15 445 0.092301
500.00 443 0.091886
160.00 425 0.088153
38.00 403 0.083590
85.00 387 0.080271
34.00 382 0.079234
52.00 375 0.077782
37.00 371 0.076952
400.00 342 0.070937
31.00 308 0.063885
170.00 306 0.063470
175.00 301 0.062433
84.00 277 0.057455
240.00 260 0.053929
72.12 241 0.049988
260.00 238 0.049366
29.00 237 0.049158
350.00 235 0.048743
210.00 216 0.044802
165.00 211 0.043765
600.00 194 0.040239
115.00 186 0.038580
36.06 182 0.037750
48.00 181 0.037543
46.00 181 0.037543
1000.00 179 0.037128
105.00 179 0.037128
120.20 171 0.035469
220.00 165 0.034224
43.00 164 0.034017
78.00 164 0.034017
62.00 157 0.032565
44.00 153 0.031735
56.00 146 0.030283
95.00 146 0.030283
150.25 144 0.029868
39.00 141 0.029246
230.00 137 0.028416
54.00 136 0.028209
41.00 135 0.028002
225.00 132 0.027379
34.85 132 0.027379
66.00 130 0.026964
135.00 120 0.024890
12.50 117 0.024268
190.00 107 0.022194
58.00 100 0.020742
9.01 91 0.018875
24.04 90 0.018668
450.00 90 0.018668
8.67 88 0.018253
155.00 86 0.017838
144.00 85 0.017631
61.00 82 0.017008
360.00 79 0.016386
47.00 78 0.016179
1.00 78 0.016179
15.02 76 0.015764
48.08 72 0.014934
63.00 71 0.014727
96.00 70 0.014519
270.00 65 0.013482
51.00 62 0.012860
53.00 58 0.012030
8.66 54 0.011201
320.00 53 0.010993
180.30 52 0.010786
68.00 51 0.010578
275.00 50 0.010371
145.00 50 0.010371
98.00 49 0.010164
74.00 48 0.009956
7.50 47 0.009749
82.00 47 0.009749
300.50 45 0.009334
57.00 44 0.009126
280.00 43 0.008919
375.00 43 0.008919
185.00 41 0.008504
330.00 40 0.008297
67.00 39 0.008089
91.00 39 0.008089
93.15 39 0.008089
112.00 39 0.008089
310.00 39 0.008089
550.00 39 0.008089
325.00 36 0.007467
700.00 36 0.007467
42.07 35 0.007260
390.00 35 0.007260
37.50 32 0.006637
800.00 31 0.006430
76.00 30 0.006223
2000.00 30 0.006223
6.02 30 0.006223
14.42 30 0.006223
2.00 28 0.005808
64.00 28 0.005808
5.33 25 0.005185
81.00 25 0.005185
132.00 25 0.005185
94.00 24 0.004978
54.09 24 0.004978
86.00 24 0.004978
92.00 24 0.004978
124.00 24 0.004978
73.00 23 0.004771
239.00 23 0.004771
6.33 23 0.004771
77.00 22 0.004563
28.84 22 0.004563
290.00 21 0.004356
156.00 21 0.004356
49.00 21 0.004356
1500.00 21 0.004356
420.00 20 0.004148
1200.00 19 0.003941
45.07 19 0.003941
102.00 19 0.003941
3000.00 18 0.003734
520.00 18 0.003734
30.12 17 0.003526
71.00 17 0.003526
59.00 17 0.003526
108.00 17 0.003526
83.00 17 0.003526
69.00 16 0.003319
122.00 16 0.003319
370.00 16 0.003319
235.00 16 0.003319
162.00 15 0.003111
365.00 15 0.003111
650.00 15 0.003111
215.00 15 0.003111
900.00 15 0.003111
601.01 14 0.002904
205.00 14 0.002904
104.00 13 0.002696
87.00 13 0.002696
195.00 13 0.002696
750.00 13 0.002696
152.00 12 0.002489
168.00 12 0.002489
144.24 12 0.002489
84.14 12 0.002489
126.00 12 0.002489
88.00 11 0.002282
340.00 11 0.002282
108.18 10 0.002074
21.03 10 0.002074
97.00 9 0.001867
106.00 9 0.001867
425.00 9 0.001867
240.40 9 0.001867
182.00 8 0.001659
9.12 8 0.001659
136.00 8 0.001659
380.00 8 0.001659
3.50 8 0.001659
33.05 8 0.001659
4.50 8 0.001659
148.00 8 0.001659
460.00 8 0.001659
224.00 8 0.001659
174.00 8 0.001659
78.13 8 0.001659
480.00 8 0.001659
8.50 7 0.001452
22.50 7 0.001452
60.24 7 0.001452
255.00 7 0.001452
134.00 7 0.001452
79.00 7 0.001452
93.00 7 0.001452
57.69 7 0.001452
5000.00 7 0.001452
116.00 7 0.001452
101.00 7 0.001452
6.50 7 0.001452
8.01 6 0.001245
360.60 6 0.001245
123.00 6 0.001245
40.05 6 0.001245
216.00 6 0.001245
300.51 6 0.001245
245.00 6 0.001245
410.00 6 0.001245
850.00 6 0.001245
17.50 6 0.001245
10.01 6 0.001245
142.00 5 0.001037
430.00 5 0.001037
32.50 5 0.001037
99.00 5 0.001037
315.00 5 0.001037
470.00 5 0.001037
90.36 5 0.001037
286.00 5 0.001037
324.00 5 0.001037
15.03 5 0.001037
210.35 5 0.001037
35.05 5 0.001037
620.00 5 0.001037
440.00 4 0.000830
166.00 4 0.000830
450.75 4 0.000830
285.00 4 0.000830
201.00 4 0.000830
3.60 4 0.000830
660.00 4 0.000830
184.00 4 0.000830
312.00 4 0.000830
27.04 4 0.000830
103.00 4 0.000830
114.00 4 0.000830
2500.00 4 0.000830
161.00 4 0.000830
118.00 4 0.000830
107.00 4 0.000830
212.00 4 0.000830
345.00 4 0.000830
1100.00 4 0.000830
236.00 4 0.000830
720.00 4 0.000830
192.00 4 0.000830
265.00 4 0.000830
117.00 4 0.000830
5.50 4 0.000830
15.20 4 0.000830
70.10 3 0.000622
1400.00 3 0.000622
89.00 3 0.000622
20.03 3 0.000622
202.00 3 0.000622
384.00 3 0.000622
6000.00 3 0.000622
151.00 3 0.000622
113.00 3 0.000622
11.50 3 0.000622
65.10 3 0.000622
121.00 3 0.000622
7.01 3 0.000622
154.00 3 0.000622
66.11 3 0.000622
22.53 3 0.000622
198.00 3 0.000622
10.50 3 0.000622
111.00 3 0.000622
305.00 3 0.000622
625.00 3 0.000622
177.00 3 0.000622
109.00 3 0.000622
131.00 3 0.000622
234.00 3 0.000622
222.00 3 0.000622
100.15 3 0.000622
137.00 3 0.000622
13.50 3 0.000622
133.00 3 0.000622
39.85 2 0.000415
7.77 2 0.000415
14.33 2 0.000415
4.20 2 0.000415
252.00 2 0.000415
45.05 2 0.000415
14.02 2 0.000415
7.21 2 0.000415
9.02 2 0.000415
189.00 2 0.000415
167.00 2 0.000415
346.00 2 0.000415
475.00 2 0.000415
36.66 2 0.000415
62.50 2 0.000415
36.05 2 0.000415
25000.00 2 0.000415
127.00 2 0.000415
725.00 2 0.000415
640.00 2 0.000415
173.00 2 0.000415
172.00 2 0.000415
157.00 2 0.000415
149.00 2 0.000415
416.00 2 0.000415
580.00 2 0.000415
16.50 2 0.000415
525.00 2 0.000415
153.00 2 0.000415
187.00 2 0.000415
204.00 2 0.000415
196.00 2 0.000415
143.00 2 0.000415
23.03 2 0.000415
540.00 2 0.000415
52.50 2 0.000415
24.50 2 0.000415
295.00 2 0.000415
138.00 2 0.000415
42.05 2 0.000415
560.00 2 0.000415
18.66 2 0.000415
18.06 2 0.000415
27.50 2 0.000415
194.00 2 0.000415
160.25 2 0.000415
7.60 1 0.000207
262.00 1 0.000207
478.00 1 0.000207
28.66 1 0.000207
20.33 1 0.000207
318.00 1 0.000207
56.25 1 0.000207
356.00 1 0.000207
468.00 1 0.000207
6.60 1 0.000207
14.50 1 0.000207
30.40 1 0.000207
446.00 1 0.000207
755.00 1 0.000207
242.00 1 0.000207
7.33 1 0.000207
96.16 1 0.000207
47.50 1 0.000207
40.02 1 0.000207
1300.00 1 0.000207
510.00 1 0.000207
128.00 1 0.000207
322.00 1 0.000207
244.00 1 0.000207
13.70 1 0.000207
256.00 1 0.000207
139.00 1 0.000207
820.00 1 0.000207
13.40 1 0.000207
333.00 1 0.000207
796.00 1 0.000207
323.00 1 0.000207
225.35 1 0.000207
214.00 1 0.000207
1360.00 1 0.000207
22.02 1 0.000207
1.20 1 0.000207
288.48 1 0.000207
34.12 1 0.000207
316.00 1 0.000207
21.50 1 0.000207
70.01 1 0.000207
505.00 1 0.000207
338.00 1 0.000207
13.02 1 0.000207
60.01 1 0.000207
1350.00 1 0.000207
120.10 1 0.000207
20.64 1 0.000207
90.75 1 0.000207
362.00 1 0.000207
52.88 1 0.000207
258.00 1 0.000207
138.15 1 0.000207
63.10 1 0.000207
31.05 1 0.000207
3.20 1 0.000207
138.23 1 0.000207
950.00 1 0.000207
60.05 1 0.000207
462.00 1 0.000207
1120.00 1 0.000207
224.24 1 0.000207
147.00 1 0.000207
218.00 1 0.000207
25.24 1 0.000207
16.25 1 0.000207
278.80 1 0.000207
129.00 1 0.000207
203.00 1 0.000207
34.84 1 0.000207
1803.03 1 0.000207
7.51 1 0.000207
570.00 1 0.000207
11.01 1 0.000207
710.00 1 0.000207
675.00 1 0.000207
70.25 1 0.000207
100.10 1 0.000207
6.25 1 0.000207
504.00 1 0.000207
17.34 1 0.000207
15.67 1 0.000207
860.00 1 0.000207
735.00 1 0.000207
183.00 1 0.000207
206.14 1 0.000207
10.57 1 0.000207
39.99 1 0.000207
6.61 1 0.000207
38.06 1 0.000207
114.15 1 0.000207
595.00 1 0.000207
243.00 1 0.000207
2250.00 1 0.000207
2400.00 1 0.000207
60.15 1 0.000207
313.00 1 0.000207
385.00 1 0.000207
150.15 1 0.000207
66.50 1 0.000207
81.10 1 0.000207
335.00 1 0.000207
304.00 1 0.000207
19.03 1 0.000207
2.99 1 0.000207
64.90 1 0.000207
451.00 1 0.000207
20.83 1 0.000207
455.00 1 0.000207
44.06 1 0.000207
185.25 1 0.000207
6.31 1 0.000207
30.50 1 0.000207
39.01 1 0.000207
306.51 1 0.000207
13.22 1 0.000207
126.15 1 0.000207
102.17 1 0.000207
15000.00 1 0.000207
450.50 1 0.000207
320.50 1 0.000207
59.02 1 0.000207
112.50 1 0.000207
278.00 1 0.000207
530.00 1 0.000207
90.10 1 0.000207
182.50 1 0.000207
28.34 1 0.000207
75.10 1 0.000207
146.00 1 0.000207
576.20 1 0.000207
125.20 1 0.000207
180.20 1 0.000207
5.30 1 0.000207
186.00 1 0.000207
6.66 1 0.000207
207.00 1 0.000207
11.66 1 0.000207
23.50 1 0.000207
71.96 1 0.000207
228.00 1 0.000207
565.00 1 0.000207
65.86 1 0.000207
16.67 1 0.000207
70.24 1 0.000207
272.00 1 0.000207
306.00 1 0.000207
10.25 1 0.000207
216.36 1 0.000207
1250.00 1 0.000207
67.50 1 0.000207
717.00 1 0.000207
302.00 1 0.000207
7.25 1 0.000207
10.33 1 0.000207
2.50 1 0.000207
171.00 1 0.000207
17.73 1 0.000207
20.01 1 0.000207
248.00 1 0.000207
33.33 1 0.000207
52.58 1 0.000207
4.66 1 0.000207
1502.53 1 0.000207
585.00 1 0.000207
136.50 1 0.000207
193.00 1 0.000207
181.00 1 0.000207
289.00 1 0.000207
382.00 1 0.000207
253.00 1 0.000207
112.12 1 0.000207
9.50 1 0.000207
4.63 1 0.000207
13.33 1 0.000207
9.61 1 0.000207
740.00 1 0.000207
7.05 1 0.000207
274.00 1 0.000207
50.40 1 0.000207
16.60 1 0.000207
790.00 1 0.000207
576.00 1 0.000207
8000.00 1 0.000207
159.00 1 0.000207
241.00 1 0.000207
4.99 1 0.000207
875.00 1 0.000207
590.00 1 0.000207
16.53 1 0.000207
3.33 1 0.000207
8.30 1 0.000207
8.33 1 0.000207
602.00 1 0.000207
252.36 1 0.000207
1442.43 1 0.000207
337.00 1 0.000207
158.00 1 0.000207
229.00 1 0.000207
75.12 1 0.000207
1750.00 1 0.000207
19.50 1 0.000207
3600.00 1 0.000207
77.10 1 0.000207
1620.00 1 0.000207
10.05 1 0.000207
41.06 1 0.000207
37.06 1 0.000207
169.00 1 0.000207
232.00 1 0.000207
164.00 1 0.000207
31.84 1 0.000207
9.20 1 0.000207
8.41 1 0.000207
32.05 1 0.000207
40.06 1 0.000207
415.00 1 0.000207
175.25 1 0.000207
116.66 1 0.000207
20.43 1 0.000207
19.83 1 0.000207
1202.02 1 0.000207
7.81 1 0.000207
311.00 1 0.000207
npo02__lastoppamount__c: importe de la ultima aportacion.
Se puede observar que casi no tiene vacios, para los donantes recurrentes casi 100% de los casos coincidirá con la donacion de socio, por lo que se descarta.
Analsis de distribución por variables
-> npsp__last_soft_credit_amount__c: Variable numerica
In [441]:
# Vamos a realizar analisis por cada variable
var = "npsp__last_soft_credit_amount__c"
In [442]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npsp__last_soft_credit_amount__c es 482224. Lo que supone un 100.0%
El nº de vacios para la variable npsp__last_soft_credit_amount__c es 0. Lo que supone un 0.0%
Out[442]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c']
In [443]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[443]:
# Tot % Tot
npsp__last_soft_credit_amount__c: importe de la ultima aportacion indirecta.
Se puede observar que es nula.
Analsis de distribución por variables
-> msf_annualizedquotachange__c: Variable numerica
In [444]:
# Vamos a realizar analisis por cada variable
var = "msf_annualizedquotachange__c"
In [445]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_annualizedquotachange__c es 5244. Lo que supone un 1.0874614287136268%
El nº de vacios para la variable msf_annualizedquotachange__c es 0. Lo que supone un 0.0%
In [446]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[446]:
# Tot % Tot
48.00 179597 37.652941
72.00 41191 8.635792
60.00 39853 8.355277
24.00 36904 7.737012
120.00 25986 5.448027
36.00 23779 4.985324
84.00 20851 4.371462
50.00 9979 2.092121
30.00 7005 1.468615
144.00 6938 1.454568
40.00 5109 1.071114
25.00 4819 1.010315
45.00 4380 0.918277
108.00 3631 0.761248
20.00 3500 0.733783
15.00 3187 0.668162
28.00 3157 0.661873
10.00 2964 0.621410
64.00 2834 0.594155
35.88 2638 0.553063
96.00 2623 0.549918
35.00 2397 0.502537
12.00 1940 0.406726
100.00 1761 0.369198
70.00 1679 0.352006
20.04 1517 0.318043
52.00 1511 0.316785
8.00 1479 0.310076
7.00 1476 0.309447
5.00 1469 0.307979
90.00 1401 0.293723
56.00 1357 0.284498
240.00 1239 0.259759
29.90 1135 0.237955
6.00 1109 0.232505
2.00 1033 0.216571
47.80 944 0.197912
18.00 941 0.197283
80.00 914 0.191622
55.00 893 0.187220
132.00 831 0.174221
14.95 809 0.169609
88.00 715 0.149901
16.00 709 0.148644
44.00 668 0.140048
47.76 632 0.132500
180.00 603 0.126420
168.00 598 0.125372
65.00 584 0.122437
32.00 548 0.114890
76.00 548 0.114890
119.40 502 0.105246
17.00 457 0.095811
22.00 456 0.095601
14.00 436 0.091408
59.64 434 0.090989
0.00 421 0.088264
33.00 414 0.086796
44.85 373 0.078200
140.00 367 0.076942
54.00 362 0.075894
42.00 357 0.074846
192.00 346 0.072540
71.60 321 0.067298
27.00 296 0.062057
156.00 294 0.061638
200.00 289 0.060590
160.00 260 0.054510
4.00 249 0.052203
34.00 244 0.051155
32.88 223 0.046752
8.97 193 0.040463
68.00 181 0.037947
11.00 180 0.037737
21.00 172 0.036060
360.00 171 0.035851
41.00 171 0.035851
44.80 157 0.032915
9.00 156 0.032706
110.00 141 0.029561
23.92 135 0.028303
59.75 127 0.026626
130.00 127 0.026626
142.80 123 0.025787
300.00 123 0.025787
31.00 114 0.023900
480.00 105 0.022014
55.76 103 0.021594
26.00 96 0.020127
51.96 90 0.018869
58.00 89 0.018659
3.00 88 0.018449
19.00 85 0.017820
47.88 83 0.017401
62.00 81 0.016982
99.40 78 0.016353
17.94 76 0.015934
38.00 74 0.015514
17.15 69 0.014466
11.96 63 0.013208
75.00 63 0.013208
104.00 58 0.012160
49.70 56 0.011741
40.08 56 0.011741
46.00 56 0.011741
52.60 51 0.010692
47.84 51 0.010692
105.00 46 0.009644
89.50 44 0.009225
83.52 42 0.008805
53.00 41 0.008596
400.00 39 0.008176
66.00 37 0.007757
13.00 36 0.007547
47.00 34 0.007128
46.85 34 0.007128
600.00 33 0.006919
37.00 33 0.006919
35.76 31 0.006499
5.98 30 0.006290
95.00 30 0.006290
720.00 27 0.005661
43.00 25 0.005241
150.00 24 0.005032
32.04 24 0.005032
51.00 23 0.004822
49.00 22 0.004612
119.00 22 0.004612
2.99 22 0.004612
66.96 21 0.004403
119.28 20 0.004193
63.72 20 0.004193
55.68 19 0.003983
178.20 19 0.003983
15.96 19 0.003983
125.00 18 0.003774
78.00 18 0.003774
139.20 17 0.003564
27.92 17 0.003564
1200.00 16 0.003354
118.99 16 0.003354
320.00 16 0.003354
92.00 16 0.003354
112.00 15 0.003145
85.00 15 0.003145
51.72 14 0.002935
63.64 14 0.002935
228.00 13 0.002725
59.00 12 0.002516
121.80 12 0.002516
26.91 12 0.002516
40.86 12 0.002516
107.40 11 0.002306
107.04 11 0.002306
166.56 10 0.002097
14.16 10 0.002097
280.00 10 0.002097
57.00 10 0.002097
118.56 10 0.002097
51.82 9 0.001887
36.87 9 0.001887
69.60 9 0.001887
56.64 8 0.001677
39.00 8 0.001677
20.93 8 0.001677
46.56 8 0.001677
124.00 8 0.001677
237.60 7 0.001468
23.00 7 0.001468
71.64 7 0.001468
95.16 7 0.001468
29.76 6 0.001258
28.31 6 0.001258
39.88 6 0.001258
45.60 6 0.001258
216.00 6 0.001258
420.00 6 0.001258
29.00 6 0.001258
204.00 6 0.001258
26.32 5 0.001048
51.80 5 0.001048
357.00 5 0.001048
1440.00 5 0.001048
47.52 5 0.001048
960.00 5 0.001048
94.00 5 0.001048
276.00 5 0.001048
238.00 4 0.000839
51.84 4 0.000839
19.95 4 0.000839
97.92 4 0.000839
21.93 4 0.000839
6.58 4 0.000839
47.64 4 0.000839
1000.00 4 0.000839
114.00 4 0.000839
44.64 4 0.000839
116.00 4 0.000839
59.65 4 0.000839
135.00 4 0.000839
67.00 4 0.000839
220.00 4 0.000839
74.00 4 0.000839
63.00 4 0.000839
89.49 4 0.000839
41.88 4 0.000839
126.00 4 0.000839
115.00 4 0.000839
49.85 3 0.000629
33.89 3 0.000629
2400.00 3 0.000629
260.00 3 0.000629
21.60 3 0.000629
59.76 3 0.000629
16.95 3 0.000629
129.25 3 0.000629
128.00 3 0.000629
136.00 3 0.000629
800.00 3 0.000629
68.04 3 0.000629
82.00 3 0.000629
106.00 3 0.000629
16.80 3 0.000629
79.50 3 0.000629
148.00 3 0.000629
14.88 3 0.000629
118.80 2 0.000419
288.00 2 0.000419
3.99 2 0.000419
500.00 2 0.000419
75.60 2 0.000419
44.90 2 0.000419
17.34 2 0.000419
33.48 2 0.000419
31.90 2 0.000419
162.00 2 0.000419
324.00 2 0.000419
145.00 2 0.000419
64.08 2 0.000419
43.88 2 0.000419
24.12 2 0.000419
13.96 2 0.000419
356.40 2 0.000419
3.34 2 0.000419
6.68 2 0.000419
38.76 2 0.000419
102.00 2 0.000419
25.04 2 0.000419
83.00 2 0.000419
540.00 2 0.000419
60.60 2 0.000419
780.00 2 0.000419
34.90 2 0.000419
20.95 2 0.000419
44.04 2 0.000419
714.00 2 0.000419
122.40 1 0.000210
154.00 1 0.000210
83.60 1 0.000210
13.60 1 0.000210
115.20 1 0.000210
119.20 1 0.000210
141.12 1 0.000210
264.00 1 0.000210
44.40 1 0.000210
20.40 1 0.000210
59.80 1 0.000210
86.00 1 0.000210
713.88 1 0.000210
28.80 1 0.000210
100.08 1 0.000210
59.28 1 0.000210
63.60 1 0.000210
384.00 1 0.000210
39.60 1 0.000210
4.50 1 0.000210
43.84 1 0.000210
143.76 1 0.000210
840.00 1 0.000210
28.68 1 0.000210
440.00 1 0.000210
51.77 1 0.000210
29.95 1 0.000210
65.76 1 0.000210
3600.00 1 0.000210
81.12 1 0.000210
138.00 1 0.000210
51.85 1 0.000210
237.96 1 0.000210
202.20 1 0.000210
190.80 1 0.000210
43.86 1 0.000210
64.32 1 0.000210
5.50 1 0.000210
29.85 1 0.000210
560.00 1 0.000210
6.98 1 0.000210
55.92 1 0.000210
87.00 1 0.000210
580.00 1 0.000210
97.00 1 0.000210
59.70 1 0.000210
60.48 1 0.000210
8.66 1 0.000210
44.25 1 0.000210
49.90 1 0.000210
296.97 1 0.000210
52.64 1 0.000210
135.44 1 0.000210
64.65 1 0.000210
27.88 1 0.000210
41.16 1 0.000210
60.04 1 0.000210
17.95 1 0.000210
109.25 1 0.000210
52.78 1 0.000210
900.00 1 0.000210
59.85 1 0.000210
29.99 1 0.000210
277.60 1 0.000210
34.99 1 0.000210
475.20 1 0.000210
81.52 1 0.000210
71.76 1 0.000210
53.64 1 0.000210
16.44 1 0.000210
640.00 1 0.000210
432.00 1 0.000210
28.98 1 0.000210
127.28 1 0.000210
15.92 1 0.000210
91.92 1 0.000210
178.80 1 0.000210
348.00 1 0.000210
250.00 1 0.000210
16.08 1 0.000210
179.00 1 0.000210
297.47 1 0.000210
166.68 1 0.000210
165.12 1 0.000210
55.80 1 0.000210
77.00 1 0.000210
53.82 1 0.000210
122.00 1 0.000210
98.56 1 0.000210
47.83 1 0.000210
350.00 1 0.000210
74.04 1 0.000210
5.60 1 0.000210
297.60 1 0.000210
103.44 1 0.000210
32.10 1 0.000210
61.68 1 0.000210
129.00 1 0.000210
50.68 1 0.000210
67.76 1 0.000210
48.85 1 0.000210
139.00 1 0.000210
32.28 1 0.000210
131.88 1 0.000210
83.49 1 0.000210
71.88 1 0.000210
57.36 1 0.000210
63.24 1 0.000210
87.52 1 0.000210
10.50 1 0.000210
69.00 1 0.000210
143.40 1 0.000210
236.00 1 0.000210
32.14 1 0.000210
81.00 1 0.000210
52.88 1 0.000210
56.04 1 0.000210
52.68 1 0.000210
35.40 1 0.000210
24.60 1 0.000210
67.64 1 0.000210
33.04 1 0.000210
660.00 1 0.000210
52.80 1 0.000210
52.08 1 0.000210
28.08 1 0.000210
1600.00 1 0.000210
372.00 1 0.000210
158.60 1 0.000210
257.57 1 0.000210
700.00 1 0.000210
15.95 1 0.000210
37.90 1 0.000210
19.80 1 0.000210
63.76 1 0.000210
61.00 1 0.000210
63.80 1 0.000210
109.92 1 0.000210
43.08 1 0.000210
147.72 1 0.000210
38.28 1 0.000210
19.94 1 0.000210
64.75 1 0.000210
133.36 1 0.000210
1320.00 1 0.000210
66.84 1 0.000210
40.68 1 0.000210
197.98 1 0.000210
89.00 1 0.000210
msf_annualizedquotachange__c: incremento de cuota anualizado que se le pediria.
Solo existe un 1% de registros a vacio, pero se va a utilizar informacion de las tablas recurrong donation y modificacion de cuota.
Analsis de distribución por variables
-> msf_relationshiplevel__c: Variable categorica
In [447]:
# Vamos a realizar analisis por cada variable
var = "msf_relationshiplevel__c"
In [448]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_relationshiplevel__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_relationshiplevel__c es 4. Lo que supone un 0.0008294900295298452%
In [449]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[449]:
# Tot % Tot
a0l0O00000k727RQAQ 459558 95.299695
a0l0O00000k727SQAQ 17380 3.604134
a0l0O00000k727TQAQ 4988 1.034374
a0l0O00000k727UQAQ 169 0.035046
a0l0O00000k727QQAQ 125 0.025922
4 0.000829
msf_relationshiplevel__c: tipo de relacion que se desea con el contacto.
Se puede observar que casi no hay vacios pero casi todos se acumulan en un valor.
Analsis de distribución por variables
-> msf_ltvcont__c: Variable numerica
In [450]:
# Vamos a realizar analisis por cada variable
var = "msf_ltvcont__c"
In [451]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_ltvcont__c es 825. Lo que supone un 0.17108231859053055%
El nº de vacios para la variable msf_ltvcont__c es 0. Lo que supone un 0.0%
In [452]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[452]:
# Tot % Tot
60.00 2118 0.439968
120.00 1991 0.413586
600.00 1773 0.368302
300.00 1769 0.367471
240.00 1660 0.344828
... ... ...
3710.16 1 0.000208
2941.45 1 0.000208
426.21 1 0.000208
10741.52 1 0.000208
1628.70 1 0.000208

58883 rows × 2 columns

msf_ltvcont__c: valor de todas las aportaciones.
Se puede observar que casi no tiene registros a nuo o vacio. Se analizará su inclusion como variable.
Analsis de distribución por variables
-> msf_ltvdesc__c: Variable categorica
In [453]:
# Vamos a realizar analisis por cada variable
var = "msf_ltvdesc__c"
In [454]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_ltvdesc__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_ltvdesc__c es 0. Lo que supone un 0.0%
In [455]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[455]:
# Tot % Tot
Alto 1.000 - 3.000 186746 38.725986
Alto 500 - 1.000 102130 21.178954
Muy Alto 3.000 - 10.000 72328 14.998839
Medio 180 - 500 69018 14.312436
Bajo 120 - 180 13592 2.818607
Muy bajo 0,10 - 50 13482 2.795796
Muy bajo 50 - 100 12686 2.630728
10.000+ 7412 1.537045
Muy bajo 100 - 120 4005 0.830527
Nulo 825 0.171082
msf_ltvcont__c: descriptivo valor de todas las aportaciones.
Se puede observar que casi no tiene registros a nuo o vacio. Se analizará su inclusion como variable.
Analsis de distribución por variables
-> mailingstate: Variable categorica
In [456]:
# Vamos a realizar analisis por cada variable
var = "mailingstate"
In [457]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable mailingstate es 0. Lo que supone un 0.0%
El nº de vacios para la variable mailingstate es 13102. Lo que supone un 2.7169945917250073%
In [458]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[458]:
# Tot % Tot
MADRID 80494 16.692243
BARCELONA 52525 10.892241
VALENCIA/VALÈNCIA 24584 5.098046
BIZKAIA 21315 4.420145
GIPUZKOA 13693 2.839552
SEVILLA 13552 2.810312
ALICANTE/ALACANT 13164 2.729852
A CORUÑA 13121 2.720935
13102 2.716995
MÁLAGA 12957 2.686926
ILLES BALEARS 10427 2.162273
PONTEVEDRA 10233 2.122043
Madrid 9475 1.964855
ASTURIAS 9413 1.951997
MURCIA 9085 1.883979
CÁDIZ 7584 1.572713
Barcelona 7116 1.475663
ZARAGOZA 6807 1.411585
SANTA CRUZ DE TENERIFE 6518 1.351654
GRANADA 6473 1.342322
LAS PALMAS 6123 1.269742
NAVARRA 6087 1.262276
CANTABRIA 5514 1.143452
ARABA/ÁLAVA 5306 1.100319
VALLADOLID 5236 1.085802
GIRONA 5048 1.046816
TARRAGONA 4371 0.906425
CASTELLÓN/CASTELLÓ 4279 0.887347
LEÓN 3533 0.732647
CÓRDOBA 3425 0.710251
TOLEDO 3204 0.664422
BURGOS 3037 0.629790
HUELVA 2917 0.604906
Alicante/Alacant 2872 0.595574
Vizcaya 2859 0.592878
BADAJOZ 2843 0.589560
CIUDAD REAL 2615 0.542279
Sevilla 2579 0.534814
LA RIOJA 2459 0.509929
LLEIDA 2424 0.502671
Murcia 2397 0.497072
JAÉN 2282 0.473224
ALMERÍA 2173 0.450620
SALAMANCA 2140 0.443777
GUADALAJARA 2079 0.431127
CÁCERES 2002 0.415160
OURENSE 1945 0.403340
LUGO 1909 0.395874
A Coruña 1886 0.391105
Malaga 1877 0.389238
ALBACETE 1857 0.385091
Valencia 1820 0.377418
Valencia/Valencia 1781 0.369330
Granada 1611 0.334077
Cadiz 1594 0.330552
HUESCA 1587 0.329100
SEGOVIA 1269 0.263156
Alicante 1195 0.247810
PALENCIA 1132 0.234746
ÁVILA 997 0.206750
CUENCA 996 0.206543
ZAMORA 983 0.203847
Málaga 915 0.189746
Guipuzcoa 910 0.188709
SORIA 842 0.174608
Santa Cruz de Tenerife 841 0.174400
Pontevedra 816 0.169216
Badajoz 816 0.169216
Asturias 798 0.165483
TERUEL 750 0.155529
Cádiz 652 0.135207
VALENCIA 636 0.131889
Las Palmas 614 0.127327
Illes Balears 614 0.127327
Navarra 600 0.124424
Bizkaia 561 0.116336
Zaragoza 557 0.115506
Tarragona 526 0.109078
Cantabria 517 0.107212
Girona 501 0.103894
Salamanca 488 0.101198
Valladolid 441 0.091451
MALAGA 441 0.091451
Huelva 432 0.089585
Valencia/València 400 0.082949
Almeria 384 0.079631
ALICANTE 361 0.074861
Santa Cruz De Tenerife 345 0.071544
Baleares 344 0.071336
Toledo 332 0.068848
Ciudad Real 332 0.068848
Guipúzcoa 301 0.062419
Gipuzkoa 300 0.062212
MELILLA 300 0.062212
Burgos 283 0.058686
CEUTA 262 0.054332
Lleida 246 0.051014
Albacete 239 0.049562
VIZCAYA 237 0.049147
Almería 228 0.047281
Lugo 223 0.046244
Guadalajara 223 0.046244
CADIZ 216 0.044792
La Rioja 215 0.044585
Cordoba 201 0.041682
Valencia/Valéncia 197 0.040852
Caceres 175 0.036290
Ourense 173 0.035875
Córdoba 169 0.035046
Castellon/Castello 167 0.034631
León 167 0.034631
Leon 158 0.032765
Jaen 156 0.032350
Huesca 154 0.031935
Segovia 148 0.030691
Jaén 143 0.029654
Castellon 142 0.029447
alava 130 0.026958
Castellón 127 0.026336
Cáceres 124 0.025714
Zamora 120 0.024885
GUIPUZCOA 114 0.023640
Alacant 107 0.022189
Álava 106 0.021981
CORDOBA 104 0.021567
Alava 104 0.021567
València 96 0.019908
CASTELLON 95 0.019700
LEON 93 0.019286
Cuenca 92 0.019078
Palencia 88 0.018249
Castellón/Castelló 87 0.018041
JAEN 73 0.015138
ALMERIA 71 0.014723
Teruel 70 0.014516
Araba/Alava 70 0.014516
CAdiz 65 0.013479
ALAVA 63 0.013064
CACERES 60 0.012442
Melilla 60 0.012442
Tenerife 59 0.012235
Soria 57 0.011820
MAlaga 55 0.011405
Ávila 48 0.009954
TENERIFE 47 0.009747
madrid 45 0.009332
BALEARES 40 0.008295
Valencia/ValEncia 40 0.008295
ISLAS BALEARES 34 0.007051
BILBAO 33 0.006843
avila 32 0.006636
GuipUzcoa 32 0.006636
AVILA 32 0.006636
Ceuta 31 0.006429
Guipuzkoa 29 0.006014
Islas Baleares 26 0.005392
Avila 24 0.004977
MALLORCA 24 0.004977
GRAN CANARIA 22 0.004562
A Coru?a 21 0.004355
LAS PALMAS DE GRAN CANARIA 21 0.004355
CANARIAS 20 0.004147
Bilbao 19 0.003940
LA CORUÑA 18 0.003733
VALENCIA/VALéNCIA 17 0.003525
GERONA 17 0.003525
CORUÑA 17 0.003525
VALENCIA/VALÉNCIA 16 0.003318
VIGO 15 0.003111
barcelona 14 0.002903
Bizcaia 14 0.002903
malaga 14 0.002903
Valencia/Valéncia 14 0.002903
PALMA DE MALLORCA 13 0.002696
Castelló 13 0.002696
valencia 13 0.002696
Guipuzcua 12 0.002488
AlmerIa 12 0.002488
ORENSE 11 0.002281
Las Palmas de Gran Canarias 11 0.002281
PAMPLONA 11 0.002281
CastellOn/CastellO 10 0.002074
sevilla 10 0.002074
IBIZA 10 0.002074
GUIPUZCUA 10 0.002074
alicante 10 0.002074
M?laga 10 0.002074
GUIPUZKOA 9 0.001866
CaDIZ 9 0.001866
OVIEDO 9 0.001866
CAceres 9 0.001866
Araba/Álava 9 0.001866
cadiz 8 0.001659
ALACANT 8 0.001659
MaLAGA 8 0.001659
LERIDA 8 0.001659
ARABA/ALAVA 8 0.001659
COrdoba 8 0.001659
asturias 7 0.001452
GALICIA 7 0.001452
Orense 7 0.001452
GUIPÚZCOA 7 0.001452
murcia 6 0.001244
GIJON 6 0.001244
LAS PALMAS DE GRAN CANARIAS 6 0.001244
salamanca 6 0.001244
MENORCA 6 0.001244
toledo 6 0.001244
Santander 6 0.001244
Araba 6 0.001244
La Coruña 6 0.001244
Vizkaya 6 0.001244
badajoz 6 0.001244
A coruña 6 0.001244
Mallorca 6 0.001244
SANTANDER 5 0.001037
Las Palmas De Gran Canaria 5 0.001037
Gerona 5 0.001037
VIZKAYA 5 0.001037
ANDORRA 5 0.001037
ÁLAVA 5 0.001037
CASTELLÓN 5 0.001037
VIZCAIA 5 0.001037
LANZAROTE 5 0.001037
Gipuzcoa 5 0.001037
Málaga 5 0.001037
Las Palmas de Gran Canaria 5 0.001037
ILLES BALEARES 5 0.001037
SAN SEBASTIAN 5 0.001037
LOGROÑO 5 0.001037
Coruña 5 0.001037
vizcaya 5 0.001037
SANTA CRUZ TENERIFE 5 0.001037
Vigo 4 0.000829
Cartagena 4 0.000829
Oviedo 4 0.000829
Illes Baleares 4 0.000829
Palma De Mallorca 4 0.000829
BArcelonA 4 0.000829
PAIS VASCO 4 0.000829
BIZCAYA 4 0.000829
valladolid 4 0.000829
SevillA 4 0.000829
Guipuzkua 4 0.000829
pontevedra 4 0.000829
AlIcante/Alacant 4 0.000829
MadrId 4 0.000829
Gran Canaria 4 0.000829
santa cruz de tenerife 4 0.000829
A CORU?A 4 0.000829
BIZCAIA 4 0.000829
CARTAGENA 4 0.000829
a coruña 3 0.000622
CáDIZ 3 0.000622
Canarias 3 0.000622
Asturia 3 0.000622
Logroño 3 0.000622
A Coruña 3 0.000622
C?diz 3 0.000622
ARABA 3 0.000622
segovia 3 0.000622
VITORIA 3 0.000622
MAdrid 3 0.000622
MurcIa 3 0.000622
Guipúzcoa 3 0.000622
?lava 3 0.000622
Pamplona 3 0.000622
DONOSTIA 3 0.000622
cantabria 3 0.000622
LA PALMA 3 0.000622
CORUÑA, A 3 0.000622
CASTELLON/CASTELLO 3 0.000622
SANTIAGO DE COMPOSTELA 3 0.000622
Lanzarote 3 0.000622
ISLAS CANARIAS 3 0.000622
LeOn 3 0.000622
GUIPUZKUA 3 0.000622
Albecete 3 0.000622
burgos 3 0.000622
STA. CRUZ DE TENERIFE 3 0.000622
CASTELLoN/CASTELLo 3 0.000622
cordoba 3 0.000622
Santa Cruz Tenerife 3 0.000622
Bizcaya 3 0.000622
C?ceres 3 0.000622
Guip?zcoa 3 0.000622
JaEn 3 0.000622
Castell?n 3 0.000622
C?rdoba 3 0.000622
Alacant / Alicante 3 0.000622
DONOSTI 3 0.000622
Valladolidad 2 0.000415
Hessen 2 0.000415
Vitoria 2 0.000415
tenerife 2 0.000415
almeria 2 0.000415
bizkaia 2 0.000415
Santa cruz de Tenerife 2 0.000415
POTEVEDRA 2 0.000415
Asturies 2 0.000415
LA PALMAS DE GRAN CANARIA 2 0.000415
ZAGAROZA 2 0.000415
jaen 2 0.000415
Portugal 2 0.000415
EXTREMADURA 2 0.000415
CORU?A 2 0.000415
Cádiz 2 0.000415
Zaragoz 2 0.000415
Malága 2 0.000415
Alicante/alacant 2 0.000415
Vizkaia 2 0.000415
LEoN 2 0.000415
CASTELLÓ 2 0.000415
FRANCIA 2 0.000415
Gijón 2 0.000415
Marbella 2 0.000415
SAN CRUZ DE TENERIFE 2 0.000415
MARBELLA 2 0.000415
MáLAGA 2 0.000415
CORUÑA,A 2 0.000415
CIUDAD 2 0.000415
EXTRANJERO 2 0.000415
ALEMANIA 2 0.000415
illes balears 2 0.000415
zaragoza 2 0.000415
GuIpuzcoa 2 0.000415
Valencia/valència 2 0.000415
SALAMNCA 2 0.000415
girona 2 0.000415
AlicAnte/AlAcAnt 2 0.000415
JAeN 2 0.000415
ACORUÑA 2 0.000415
CASTILLA Y LEON 2 0.000415
Almer?a 2 0.000415
Zaragona 2 0.000415
tarragona 2 0.000415
ASTURIA 2 0.000415
TARRRAGONA 2 0.000415
LLeida 2 0.000415
caceres 2 0.000415
Le?n 2 0.000415
lugo 2 0.000415
Islas Canarias 2 0.000415
zamora 2 0.000415
CANARIA 2 0.000415
ValencIa/ValencIa 2 0.000415
NAVARA 1 0.000207
palma de mallorca 1 0.000207
Gipizkoa 1 0.000207
Mállaga 1 0.000207
BIzkaia 1 0.000207
PONTEVDRA 1 0.000207
Gipuzloa 1 0.000207
Gipuzkua 1 0.000207
palencia 1 0.000207
balears 1 0.000207
MurciA 1 0.000207
Guizpuzcoa 1 0.000207
GORLIZ 1 0.000207
LA CORU?A 1 0.000207
Getxo/Bizkaia 1 0.000207
Illes Balers 1 0.000207
VizcAyA 1 0.000207
AvilA 1 0.000207
gijón 1 0.000207
VITORIA-GASTEIZ 1 0.000207
PAMPLOANA 1 0.000207
GELVES 1 0.000207
Islas Balears 1 0.000207
la coruña 1 0.000207
VAlenciA/VAlenciA 1 0.000207
MelillA 1 0.000207
LLIEDA 1 0.000207
MALGA 1 0.000207
Illes De Balears 1 0.000207
CaCERES 1 0.000207
Águilas 1 0.000207
Illes Ballears 1 0.000207
navarra 1 0.000207
Santiago 1 0.000207
VALENCIA/VALENCIA 1 0.000207
CORUÑA A 1 0.000207
granada 1 0.000207
Guadalaja 1 0.000207
Gipozkoa 1 0.000207
Alemania 1 0.000207
Araba/álava 1 0.000207
guipuzkoa 1 0.000207
Donosti 1 0.000207
Extremadura 1 0.000207
bilbao 1 0.000207
Peñiscola 1 0.000207
SANTA CRUZ DETENERIFE 1 0.000207
SEVIILA 1 0.000207
BIZKAIYA 1 0.000207
Roma 1 0.000207
Pontevdra 1 0.000207
Servilla 1 0.000207
PALMA MALLORCA 1 0.000207
Araba/alava 1 0.000207
GUIPOUZCOA 1 0.000207
A CoruñA 1 0.000207
OTUR VALDES (LUARCA) 1 0.000207
GRANADILLA DE ABONA 1 0.000207
GIPUSCUA 1 0.000207
santarder 1 0.000207
Seilla 1 0.000207
ciudad real 1 0.000207
GUIPUCOA 1 0.000207
LORCA 1 0.000207
PALMAS DE GRAN CANARIAS 1 0.000207
Montcada I Reixac 1 0.000207
guipuzcoa 1 0.000207
rARRAGONA 1 0.000207
Illes Belears 1 0.000207
Bsarcelona 1 0.000207
AUSTURIAS 1 0.000207
Vizacaya 1 0.000207
guipzkoa 1 0.000207
BADALONA 1 0.000207
APOLA 1 0.000207
S/C DE TENERIFE 1 0.000207
TARRAGON 1 0.000207
Gudalajara 1 0.000207
A CORUA 1 0.000207
Castello 1 0.000207
Tarrragona 1 0.000207
BIZKAYA 1 0.000207
BALERAES 1 0.000207
Castellón/Castello 1 0.000207
DE JAEN 1 0.000207
VIzcaya 1 0.000207
Guipuzcuoa 1 0.000207
Gipuzkia 1 0.000207
Valdegovía 1 0.000207
ciudda real 1 0.000207
PONTEVEDRO 1 0.000207
Pontebra 1 0.000207
Guipuscoa 1 0.000207
Bizkaya 1 0.000207
Las Palamas 1 0.000207
Araba/Álaba 1 0.000207
SALMANCA 1 0.000207
Barcleona 1 0.000207
Cantábria 1 0.000207
BALEARS 1 0.000207
Pontevendra 1 0.000207
Taragona 1 0.000207
VIZCAYIA 1 0.000207
Alicante (Alacant) 1 0.000207
VALLADOLD 1 0.000207
Tarrronga 1 0.000207
CASTILLA 1 0.000207
ARONA 1 0.000207
Alicante/Alacantt 1 0.000207
SANTA CRUZ DE TRENERIFE 1 0.000207
a Coruña 1 0.000207
BURJASOL 1 0.000207
vigo 1 0.000207
GUIPUZ 1 0.000207
Las Palma 1 0.000207
ZARAGONA 1 0.000207
BAEARES 1 0.000207
BIZAKAIA 1 0.000207
BENALMADENA 1 0.000207
CASTELLoN 1 0.000207
ALGUAZAS 1 0.000207
Garnada 1 0.000207
CORBOBA 1 0.000207
PO 1 0.000207
Vallodolid 1 0.000207
ILLESBALEARS 1 0.000207
A CORUÑA 1 0.000207
TERRAGONA 1 0.000207
Ja?n 1 0.000207
Guipizcoa 1 0.000207
Arava/Álava 1 0.000207
Zarago 1 0.000207
Santa Cruz De Tenerfie 1 0.000207
MIERES 1 0.000207
araba 1 0.000207
LA RIJOA 1 0.000207
GUPUZCOA 1 0.000207
Álava 1 0.000207
ALBECETE 1 0.000207
CORU?A,A 1 0.000207
SANTA CRUZ DE TENERIFA 1 0.000207
SAN SESBAST 1 0.000207
VICTORIA 1 0.000207
ILLES 1 0.000207
VIZAYA 1 0.000207
albacete 1 0.000207
Todelo 1 0.000207
PEILAGOS 1 0.000207
PICAXEN 1 0.000207
Guipozcoa 1 0.000207
PONTVEDRRDA 1 0.000207
ARABA/aLAVA 1 0.000207
BALEARES, ISLAS 1 0.000207
LAs PAlmAs 1 0.000207
STA LUCIA TIRAJANAGRAN CANARIA 1 0.000207
SEVILA 1 0.000207
Coto de Bornos 1 0.000207
VIZKAIA 1 0.000207
PONTEVENDRA 1 0.000207
STA DE CRUZ DE TENERIFE 1 0.000207
VALLLADOLID 1 0.000207
VIZCAA 1 0.000207
VALLADOLIS 1 0.000207
VALLADALID 1 0.000207
VALENIA 1 0.000207
GUIPOCUA 1 0.000207
PASCO VASCO 1 0.000207
PALMA 1 0.000207
Mayorca 1 0.000207
EVILLA 1 0.000207
Cádiaz 1 0.000207
DENIA 1 0.000207
LA ALBERCA 1 0.000207
a coruñpa 1 0.000207
Bizckai 1 0.000207
CANTAMBRIA 1 0.000207
Valencia/Val?ncia 1 0.000207
VIZACAYA 1 0.000207
TARAGONA 1 0.000207
Almería 1 0.000207
Castellón/Castelló 1 0.000207
MOTRIL 1 0.000207
guipuzcua 1 0.000207
MUERCIA 1 0.000207
POLA DE LENA-ASTURIAS 1 0.000207
Francia 1 0.000207
CATALUÑA 1 0.000207
AlIcante 1 0.000207
VALLODOLID 1 0.000207
SEVILLLA 1 0.000207
ZARAUTZ 1 0.000207
GRANADAS 1 0.000207
CASTILLA DE LEON 1 0.000207
GIPUZCUA 1 0.000207
GUALAJARA 1 0.000207
GUIPUZOCA 1 0.000207
GuIpUzcoa 1 0.000207
ANDALUCIA 1 0.000207
VIZCVAYA 1 0.000207
VITORIA GASTEIZ 1 0.000207
ISLAS BALERES 1 0.000207
GUIPUIZCOA 1 0.000207
CASTELLON DE LA PLANA 1 0.000207
FUERTEVENTURA 1 0.000207
Vizvaya 1 0.000207
Valencai 1 0.000207
Arona 1 0.000207
Murica 1 0.000207
Lerida 1 0.000207
Viscaya 1 0.000207
Las Palmas De Gran Canarias 1 0.000207
Valldolid 1 0.000207
Vlencia 1 0.000207
Andorra 1 0.000207
MALPICA DE BERGANTIÑOS 1 0.000207
badajod 1 0.000207
Alicate 1 0.000207
CastellOn 1 0.000207
Fontanarejo 1 0.000207
Mälaga 1 0.000207
SANTA EULALIA DEL RIO 1 0.000207
ZAROGAZA 1 0.000207
Matarrubia 1 0.000207
Turias 1 0.000207
Valéncia 1 0.000207
LA CARUÑA 1 0.000207
BAJADOZ 1 0.000207
LAS PALMAS (LANZAROTE) 1 0.000207
PATERNA 1 0.000207
GUIPOZKOA 1 0.000207
FELANITX 1 0.000207
CIudad Real 1 0.000207
LleIda 1 0.000207
La Coru?a 1 0.000207
SevIlla 1 0.000207
SAN SE BASTIAN 1 0.000207
PONTEVERA 1 0.000207
CALLELLON 1 0.000207
BRION 1 0.000207
baleares 1 0.000207
CUDAD REAL 1 0.000207
STA CRUZ DE TERENIFE 1 0.000207
GIPUZCOA 1 0.000207
BEJAR 1 0.000207
GUIPOZCOA 1 0.000207
La Pama 1 0.000207
FRONSAC 1 0.000207
GRANDA 1 0.000207
A Coruna 1 0.000207
AQUITANIA 1 0.000207
La rioja 1 0.000207
ILLES BALEARS MENORCA 1 0.000207
aLAVA 1 0.000207
Castilla y León 1 0.000207
ALABA 1 0.000207
ALMERiA 1 0.000207
BARCELONAc23090 1 0.000207
Alicane 1 0.000207
Alicante/Alcant 1 0.000207
Castellóna 1 0.000207
gRANADA 1 0.000207
PORTUGAL 1 0.000207
FERROL 1 0.000207
Toledo. 1 0.000207
CASTELLO 1 0.000207
Valrencia 1 0.000207
Algeciras 1 0.000207
TARRAGORRA 1 0.000207
Palma de Mallorca 1 0.000207
PARIS 1 0.000207
SUIZA 1 0.000207
Tarrgona 1 0.000207
lanzarote 1 0.000207
Sta Cruz De Tenerife 1 0.000207
sant sadurni de noia 1 0.000207
San Sebastin 1 0.000207
MAlAgA 1 0.000207
Albacte 1 0.000207
Cieza 1 0.000207
SALALANCA 1 0.000207
ALVA 1 0.000207
Castellón de la Plana 1 0.000207
Pontvendra 1 0.000207
Santa Cruz de Tenerifie 1 0.000207
Rioja,la 1 0.000207
XATIVA 1 0.000207
Luego 1 0.000207
Badiajoz 1 0.000207
S.C. TENERIFE 1 0.000207
Fuengirola 1 0.000207
Munchen 1 0.000207
CoRDOBA 1 0.000207
SANTA CRUZ DE La PALMA 1 0.000207
Paris 1 0.000207
VICTORAI GAXTEIZ 1 0.000207
LE0N 1 0.000207
Catarroja 1 0.000207
Albate 1 0.000207
Navara 1 0.000207
Las Palmas - Telde 1 0.000207
LAS PALMAS DE GRAN CANARIOS 1 0.000207
Schwieberdingen 1 0.000207
Aragón 1 0.000207
Alicante. 1 0.000207
mallorca 1 0.000207
TARRAGAONA 1 0.000207
A 1 0.000207
PALMA DE MALORCA 1 0.000207
LAS PALMAS GRAN CANARIAS 1 0.000207
LANZARATE 1 0.000207
islas Baleares 1 0.000207
ESPAÑA 1 0.000207
YEIDA 1 0.000207
Astudias 1 0.000207
RIOJA,LA 1 0.000207
GRAN CANARIAS 1 0.000207
Castellon/Castelló 1 0.000207
Barelona 1 0.000207
LAS PALMAS GRAN CANARIA 1 0.000207
Sta.cruz Tenerife 1 0.000207
bizcaia 1 0.000207
VILLAPEDRE 1 0.000207
VAL DE MARNE 1 0.000207
BABIERA 1 0.000207
GRAN CANARIAS - LAS PALMAS 1 0.000207
A CORUNA 1 0.000207
ORENZE 1 0.000207
Cáceres 1 0.000207
Corboda 1 0.000207
Cordoba Ibarruri 3 esc 1 3 1 1 0.000207
Sant vicente 1 0.000207
Balears 1 0.000207
Castilla y la Mancha 1 0.000207
Las Baleares 1 0.000207
Fuerteventura 1 0.000207
Guipuzkuo 1 0.000207
Gupuzcoa 1 0.000207
ARRASATE/MONDRAGON 1 0.000207
Elche 1 0.000207
gipuzkoa 1 0.000207
mailingstate: provincia.
Se puede observar que hay un 5% de vacios. además de muchos registros para las provincias existentes, lo que complica el uso de esta variable que necesitaría un tratamiento de datos especifico.
Analsis de distribución por variables
-> npsp__largest_soft_credit_amount__c: Variable numerica
In [459]:
# Vamos a realizar analisis por cada variable
var = "npsp__largest_soft_credit_amount__c"
In [460]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npsp__largest_soft_credit_amount__c es 482224. Lo que supone un 100.0%
El nº de vacios para la variable npsp__largest_soft_credit_amount__c es 0. Lo que supone un 0.0%
Out[460]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'npsp__largest_soft_credit_amount__c']
In [461]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[461]:
# Tot % Tot
npsp__largest_soft_credit_amount__c: mayor importe de operaciones indirectas.
Se puede observar que todos los registros con nulos.
Analsis de distribución por variables
-> npo02__soft_credit_last_year__c: Variable numerica
In [462]:
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_last_year__c"
In [463]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__soft_credit_last_year__c es 482224. Lo que supone un 100.0%
El nº de vacios para la variable npo02__soft_credit_last_year__c es 0. Lo que supone un 0.0%
Out[463]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c']
In [464]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[464]:
# Tot % Tot
npo02__soft_credit_last_year__c: operaciones indirectas el año pasado.
Se puede observar que todos los registros con nulos.
Analsis de distribución por variables
-> npo02__soft_credit_this_year__c: Variable numerica
In [465]:
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_this_year__c"
In [466]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__soft_credit_this_year__c es 482224. Lo que supone un 100.0%
El nº de vacios para la variable npo02__soft_credit_this_year__c es 0. Lo que supone un 0.0%
Out[466]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c']
In [467]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[467]:
# Tot % Tot
npo02__soft_credit_this_year__c: operaciones indirectas este año.
Se puede observar que todos los registros con nulos.
Analsis de distribución por variables
-> npo02__soft_credit_two_years_ago__c: Variable numerica
In [468]:
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_two_years_ago__c"
In [469]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__soft_credit_two_years_ago__c es 482224. Lo que supone un 100.0%
El nº de vacios para la variable npo02__soft_credit_two_years_ago__c es 0. Lo que supone un 0.0%
Out[469]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c']
In [470]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[470]:
# Tot % Tot
npo02__soft_credit_two_years_ago__c: operaciones indirectas hace 2 años.
Se puede observar que todos los registros con nulos.
Analsis de distribución por variables
-> msf_nocaptacionfondoscp__c: Variable booleana
In [471]:
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondoscp__c"
In [472]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_nocaptacionfondoscp__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_nocaptacionfondoscp__c es 0. Lo que supone un 0.0%
In [473]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[473]:
# Tot % Tot
False 406361 84.268099
True 75863 15.731901
msf_nocaptacionfondoscp__c: permiso de comuncacion por correo postal.
Se puede observar que no hay vacios.
Analsis de distribución por variables
-> msf_nocaptacionfondosemail__c: Variable booleana
In [474]:
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondosemail__c"
In [475]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_nocaptacionfondosemail__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_nocaptacionfondosemail__c es 0. Lo que supone un 0.0%
In [476]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[476]:
# Tot % Tot
False 442371 91.735583
True 39853 8.264417
msf_nocaptacionfondosemail__c: permiso de comuncacion por email.
Se puede observar que no hay vacios.
Analsis de distribución por variables
-> msf_nocaptacionfondosmi__c: Variable booleana
In [477]:
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondosmi__c"
In [478]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_nocaptacionfondosmi__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_nocaptacionfondosmi__c es 0. Lo que supone un 0.0%
In [479]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[479]:
# Tot % Tot
False 449504 93.214772
True 32720 6.785228
msf_nocaptacionfondosmi__c: permiso de comuncacion por mi.
Se puede observar que no hay vacios.
Analsis de distribución por variables
-> msf_nocaptacionfondossms__c: Variable booleana
In [480]:
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondossms__c"
In [481]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_nocaptacionfondossms__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_nocaptacionfondossms__c es 0. Lo que supone un 0.0%
In [482]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[482]:
# Tot % Tot
False 448257 92.956178
True 33967 7.043822
msf_nocaptacionfondossms__c: permiso de comuncacion por sms.
Se puede observar que no hay vacios.
Analsis de distribución por variables
-> msf_firstcampaignentryrecurringdonor__c: Variable categorica
In [483]:
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaignentryrecurringdonor__c"
In [484]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_firstcampaignentryrecurringdonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_firstcampaignentryrecurringdonor__c es 1. Lo que supone un 0.0002073725073824613%
In [485]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[485]:
# Tot % Tot
7013Y000001mrBSQAY 16831 3.490287
7013Y000001mr2cQAA 14005 2.904252
7013Y000001mr2DQAQ 12762 2.646488
7013Y000001mr1MQAQ 12257 2.541765
7013Y000001mrCzQAI 11836 2.454461
... ... ...
7013Y000001vXGOQA2 1 0.000207
7013Y000001mrE4QAI 1 0.000207
7013Y000001mrAIQAY 1 0.000207
7013Y000001mr7OQAQ 1 0.000207
7013Y000001mrY3QAI 1 0.000207

2288 rows × 2 columns

msf_firstcampaignentryrecurringdonor__c: primera campaña de colaboracion como socio recurrente.
Se puede observar que casi no hay vacios.
Analsis de distribución por variables
-> msf_firstcampaingcolaboration__c: Variable categorica
In [486]:
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaingcolaboration__c"
In [487]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_firstcampaingcolaboration__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_firstcampaingcolaboration__c es 714. Lo que supone un 0.14806397027107734%
In [488]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[488]:
# Tot % Tot
7013Y000001mrCzQAI 19195 3.980515
7013Y000001mrBSQAY 16604 3.443213
7013Y000001mr2cQAA 13224 2.742294
7013Y000001mr1MQAQ 12160 2.521650
7013Y000001mr2DQAQ 11853 2.457986
... ... ...
7013Y000001mrAMQAY 1 0.000207
7013Y000001mrTEQAY 1 0.000207
7013Y000001mrGQQAY 1 0.000207
7013Y000001mr0BQAQ 1 0.000207
7013Y000001mrY3QAI 1 0.000207

2465 rows × 2 columns

msf_firstcampaingcolaboration__c: primera campaña de colaboracion economica.
Se puede observar que hay un 4% de vacios.
Analsis de distribución por variables
-> msf_firstannualizedquota__c: Variable numerica
In [489]:
# Vamos a realizar analisis por cada variable
var = "msf_firstannualizedquota__c"
In [490]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_firstannualizedquota__c es 1. Lo que supone un 0.0002073725073824613%
El nº de vacios para la variable msf_firstannualizedquota__c es 0. Lo que supone un 0.0%
In [491]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[491]:
# Tot % Tot
1.200000e+02 140983 29.236059
6.000000e+01 58439 12.118667
1.800000e+02 52974 10.985374
2.400000e+02 26696 5.536028
7.200000e+01 25395 5.266236
1.440000e+02 21497 4.457896
7.212000e+01 16501 3.421861
3.600000e+01 10931 2.266794
3.600000e+02 9672 2.005711
9.600000e+01 8647 1.793154
1.000000e+02 8576 1.778430
3.000000e+02 7661 1.588684
5.000000e+01 7245 1.502417
5.196000e+01 6568 1.362025
6.010000e+01 4736 0.982118
4.000000e+01 4464 0.925713
3.000000e+01 4171 0.864953
8.000000e+01 4166 0.863916
2.000000e+01 4031 0.835920
3.005000e+01 3790 0.785943
8.400000e+01 3763 0.780344
1.202000e+02 3429 0.711082
2.000000e+02 3071 0.636842
1.442400e+02 2644 0.548294
4.800000e+01 2281 0.473018
1.500000e+02 2248 0.466174
2.163600e+02 2238 0.464101
6.000000e+02 2204 0.457050
1.000000e+01 1902 0.394423
1.200000e+01 1885 0.390898
3.606000e+02 1773 0.367672
1.320000e+02 1502 0.311474
1.803000e+01 1410 0.292396
1.500000e+01 1341 0.278087
2.160000e+02 1183 0.245322
9.015000e+01 1045 0.216705
7.200000e+02 1021 0.211728
2.500000e+01 995 0.206336
2.404000e+02 868 0.180000
1.080000e+02 858 0.177926
9.000000e+01 770 0.159677
4.800000e+02 747 0.154908
4.808000e+01 668 0.138525
1.600000e+02 630 0.130645
1.200000e+03 557 0.115507
3.486000e+01 547 0.113433
2.400000e+01 517 0.107212
4.000000e+02 491 0.101820
2.404000e+01 479 0.099332
1.560000e+02 441 0.091451
2.040000e+02 441 0.091451
1.502500e+02 429 0.088963
3.606000e+01 387 0.080253
1.394400e+02 379 0.078594
1.040400e+02 370 0.076728
1.920000e+02 359 0.074447
7.000000e+01 346 0.071751
7.212000e+02 336 0.069677
7.500000e+01 313 0.064908
1.082400e+02 300 0.062212
3.612000e+01 269 0.055783
2.500000e+02 264 0.054746
1.803600e+02 237 0.049147
1.680000e+02 226 0.046866
4.200000e+02 204 0.042304
5.000000e+02 198 0.041060
9.316000e+01 187 0.038779
1.039200e+02 175 0.036290
6.010000e+00 165 0.034217
1.400000e+02 162 0.033594
9.616000e+01 152 0.031521
2.520000e+02 148 0.030691
1.803000e+02 146 0.030276
1.202000e+01 143 0.029654
2.640000e+02 140 0.029032
2.884800e+02 131 0.027166
2.880000e+02 128 0.026544
5.000000e+00 122 0.025299
5.200000e+01 122 0.025299
1.730400e+02 121 0.025092
3.608000e+01 118 0.024470
7.224000e+01 117 0.024263
3.200000e+01 105 0.021774
5.400000e+02 99 0.020530
1.000000e+03 95 0.019700
3.005100e+02 94 0.019493
4.183200e+02 90 0.018664
5.768000e+01 89 0.018456
1.800000e+01 89 0.018456
3.500000e+01 85 0.017627
3.000000e+00 79 0.016382
1.250000e+02 78 0.016175
6.012000e+01 72 0.014931
4.207000e+01 71 0.014723
1.800000e+03 71 0.014723
4.500000e+01 67 0.013894
4.320000e+02 66 0.013687
2.885000e+01 62 0.012857
6.000000e+00 60 0.012442
1.923200e+02 54 0.011198
8.414000e+01 50 0.010369
1.300000e+02 48 0.009954
1.081800e+03 45 0.009332
8.000000e+00 44 0.009124
1.080000e+03 43 0.008917
5.409000e+01 41 0.008502
1.442000e+01 41 0.008502
0.000000e+00 40 0.008295
9.600000e+02 40 0.008295
3.120000e+02 40 0.008295
2.400000e+03 40 0.008295
4.327200e+02 39 0.008088
6.010000e+02 39 0.008088
5.500000e+01 38 0.007880
8.000000e+02 38 0.007880
1.154000e+02 37 0.007673
1.100000e+02 37 0.007673
1.500000e+03 35 0.007258
4.200000e+01 35 0.007258
4.808000e+02 34 0.007051
5.770000e+01 34 0.007051
9.000000e+02 34 0.007051
8.400000e+02 34 0.007051
3.462000e+02 33 0.006843
6.010100e+02 32 0.006636
1.440000e+03 32 0.006636
3.960000e+02 30 0.006221
2.760000e+02 30 0.006221
1.081800e+02 30 0.006221
3.726400e+02 28 0.005806
3.200000e+02 27 0.005599
3.600000e+03 26 0.005392
1.682800e+02 25 0.005184
1.040000e+02 25 0.005184
2.404100e+02 24 0.004977
3.614400e+02 23 0.004770
1.803200e+02 23 0.004770
2.524800e+02 23 0.004770
3.500000e+02 23 0.004770
3.000000e+03 22 0.004562
5.769600e+02 21 0.004355
6.500000e+01 21 0.004355
2.800000e+02 21 0.004355
5.048400e+02 20 0.004147
2.200000e+02 20 0.004147
3.840000e+02 19 0.003940
3.240000e+02 19 0.003940
3.650000e+02 18 0.003733
2.280000e+02 17 0.003525
1.204800e+02 17 0.003525
1.700000e+02 17 0.003525
2.000000e+03 17 0.003525
8.800000e+01 17 0.003525
1.082000e+02 17 0.003525
5.400000e+01 17 0.003525
2.800000e+01 17 0.003525
8.654400e+02 16 0.003318
1.204000e+01 16 0.003318
1.600000e+01 16 0.003318
5.040000e+02 16 0.003318
6.000000e+03 15 0.003111
9.020000e+00 15 0.003111
1.442400e+03 15 0.003111
2.600000e+02 14 0.002903
1.094400e+02 14 0.002903
3.360000e+02 14 0.002903
6.024000e+01 14 0.002903
5.600000e+01 14 0.002903
8.416000e+01 14 0.002903
3.606100e+02 13 0.002696
6.600000e+01 13 0.002696
9.200000e+01 13 0.002696
7.813000e+01 12 0.002488
8.500000e+01 11 0.002281
3.720000e+02 11 0.002281
4.080000e+02 11 0.002281
1.520000e+02 11 0.002281
8.460000e+01 11 0.002281
3.800000e+01 11 0.002281
6.120000e+02 10 0.002074
1.480000e+02 10 0.002074
2.103500e+02 9 0.001866
6.600000e+02 9 0.001866
7.800000e+01 9 0.001866
3.900000e+01 9 0.001866
1.750000e+02 9 0.001866
3.480000e+02 8 0.001659
1.824000e+02 8 0.001659
4.500000e+02 8 0.001659
1.120000e+02 8 0.001659
1.400000e+01 8 0.001659
4.680000e+02 8 0.001659
2.308000e+02 8 0.001659
8.640000e+02 8 0.001659
1.450000e+02 7 0.001452
3.012000e+01 7 0.001452
6.400000e+01 7 0.001452
8.652000e+01 7 0.001452
2.160000e+03 7 0.001452
7.000000e+00 7 0.001452
6.240000e+02 7 0.001452
7.600000e+01 7 0.001452
1.020000e+02 7 0.001452
2.200000e+01 7 0.001452
3.400000e+01 6 0.001244
4.508000e+01 6 0.001244
7.228800e+02 6 0.001244
6.800000e+01 6 0.001244
1.503000e+01 6 0.001244
4.400000e+01 6 0.001244
4.332000e+01 6 0.001244
6.200000e+01 6 0.001244
2.160000e+01 6 0.001244
9.036000e+01 6 0.001244
1.280000e+02 6 0.001244
3.010000e+00 6 0.001244
1.020000e+03 5 0.001037
2.250000e+02 5 0.001037
7.920000e+02 5 0.001037
3.005200e+02 5 0.001037
1.803000e+03 5 0.001037
1.350000e+02 5 0.001037
1.200000e+04 5 0.001037
5.289000e+01 5 0.001037
9.016000e+01 5 0.001037
7.400000e+01 5 0.001037
1.050000e+02 5 0.001037
5.200000e+02 5 0.001037
2.600000e+01 5 0.001037
1.719600e+02 5 0.001037
2.404040e+03 4 0.000829
1.600000e+03 4 0.000829
6.924000e+02 4 0.000829
7.000000e+02 4 0.000829
5.592000e+01 4 0.000829
7.300000e+01 4 0.000829
4.800000e+03 4 0.000829
8.660000e+00 4 0.000829
9.012000e+01 4 0.000829
7.210000e+00 4 0.000829
1.201200e+02 4 0.000829
1.360000e+02 4 0.000829
1.300000e+01 4 0.000829
9.000000e+00 4 0.000829
7.932000e+01 4 0.000829
1.202040e+03 4 0.000829
7.800000e+02 4 0.000829
7.200000e+03 4 0.000829
1.160000e+02 4 0.000829
2.100000e+02 4 0.000829
1.532600e+02 3 0.000622
2.700000e+01 3 0.000622
5.000000e+03 3 0.000622
2.115600e+02 3 0.000622
1.117920e+03 3 0.000622
4.000000e+00 3 0.000622
5.100000e+01 3 0.000622
9.375600e+02 3 0.000622
3.606120e+03 3 0.000622
1.502400e+02 3 0.000622
3.005000e+02 3 0.000622
2.104000e+01 3 0.000622
3.700000e+01 3 0.000622
1.250000e+01 3 0.000622
1.803040e+03 3 0.000622
3.125200e+02 3 0.000622
6.400000e+02 3 0.000622
1.700000e+01 3 0.000622
1.320000e+03 3 0.000622
1.444000e+01 3 0.000622
4.560000e+02 3 0.000622
9.900000e+01 3 0.000622
1.650000e+02 3 0.000622
4.507600e+02 3 0.000622
2.700000e+02 2 0.000415
1.502600e+02 2 0.000415
2.884000e+01 2 0.000415
1.081200e+02 2 0.000415
3.300000e+01 2 0.000415
6.360000e+02 2 0.000415
3.666000e+01 2 0.000415
2.480000e+02 2 0.000415
7.356000e+02 2 0.000415
7.513000e+01 2 0.000415
3.365600e+02 2 0.000415
1.260000e+02 2 0.000415
1.212000e+03 2 0.000415
1.983600e+02 2 0.000415
5.772000e+01 2 0.000415
1.260000e+03 2 0.000415
9.015200e+02 2 0.000415
1.740000e+02 2 0.000415
4.600000e+02 2 0.000415
7.200000e+00 2 0.000415
3.250000e+02 2 0.000415
3.996000e+01 2 0.000415
2.300000e+01 2 0.000415
9.996000e+01 2 0.000415
5.988000e+01 2 0.000415
1.640000e+02 2 0.000415
2.884920e+03 2 0.000415
1.322400e+02 2 0.000415
3.300000e+02 2 0.000415
4.600000e+01 2 0.000415
7.200000e-01 2 0.000415
2.900000e+01 2 0.000415
1.560000e+03 2 0.000415
2.040000e+03 2 0.000415
2.163600e+03 2 0.000415
9.360000e+02 2 0.000415
1.510000e+02 2 0.000415
1.732000e+01 2 0.000415
2.004000e+03 2 0.000415
5.300000e+01 2 0.000415
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1.400000e+03 2 0.000415
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1.230000e+02 2 0.000415
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4.300000e+01 2 0.000415
3.660000e+02 2 0.000415
1.620000e+02 2 0.000415
5.408000e+01 2 0.000415
1.355880e+03 2 0.000415
2.750000e+02 2 0.000415
7.560000e+02 2 0.000415
4.328000e+01 2 0.000415
1.154400e+02 2 0.000415
1.586400e+02 2 0.000415
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1.444800e+02 1 0.000207
3.602400e+02 1 0.000207
2.440000e+02 1 0.000207
3.900000e+03 1 0.000207
4.520000e+02 1 0.000207
2.046000e+02 1 0.000207
5.950000e+01 1 0.000207
1.092000e+02 1 0.000207
2.282400e+03 1 0.000207
1.220000e+02 1 0.000207
msf_firstannualizedquota__c: importe anualizado del primer compromiso como socio.
Se puede observar que existe un 3% de nulos, los importes más comunes van desde 60 a 240€
Analsis de distribución por variables
-> msf_program__c: Variable categorica
In [492]:
# Vamos a realizar analisis por cada variable
var = "msf_program__c"
In [493]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_program__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_program__c es 129. Lo que supone un 0.026751053452337505%
In [494]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[494]:
# Tot % Tot
Cultivación socios MASS 432993 89.790844
Retención 1r año MASS 24150 5.008046
Cultivación socios MID 17102 3.546485
Mid+ Donors 4248 0.880918
Empresas y Colectivos Mass 2262 0.469077
Testamentarios 624 0.129400
Retención 1r año MID 188 0.038986
Otros programas transversales 134 0.027788
129 0.026751
Otros 12Few+ 116 0.024055
Empresas y Colectivos Mid, Mid + 83 0.017212
Públicos Especiales 65 0.013479
Potenciales a Major Donors 52 0.010783
Major Donors 36 0.007465
Empresas y Colectivos Estratégicas 16 0.003318
Instituciones Públicas Mass 12 0.002488
Fundaciones Mass 7 0.001452
Reactivación bajas MASS 3 0.000622
Cultivación/conversión Donantes MASS 2 0.000415
Fundaciones Mid, Mid + 1 0.000207
Reactivación/conversión EXDonantes MASS 1 0.000207
msf_program__c: programa al que pertenece.
Se puede observar que la variable tiene un 3% de vacios dividiendose principalmente en dos.
Analsis de distribución por variables
-> msf_programaherencias__c: Variable booleana
In [495]:
# Vamos a realizar analisis por cada variable
var = "msf_programaherencias__c"
In [496]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_programaherencias__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_programaherencias__c es 0. Lo que supone un 0.0%
In [497]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[497]:
# Tot % Tot
False 478318 99.190003
True 3906 0.809997
msf_programaherencias__c: indicador de algun tipo de relacion con el programa de herencias.
Se puede observar que toma el valor de falso en casi todos los casos.
Analsis de distribución por variables
-> msf_programais__c: Variable booleana
In [498]:
# Vamos a realizar analisis por cada variable
var = "msf_programais__c"
In [499]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_programais__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_programais__c es 0. Lo que supone un 0.0%
In [500]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[500]:
# Tot % Tot
False 481983 99.950023
True 241 0.049977
msf_programais__c: indicador de promotor en iniciativa solidaria.
Se puede observar que toma el valor de falso en casi todos los casos.
Analsis de distribución por variables
-> msf_pressurecomplaint__c: Variable booleana
In [501]:
# Vamos a realizar analisis por cada variable
var = "msf_pressurecomplaint__c"
In [502]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_pressurecomplaint__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_pressurecomplaint__c es 0. Lo que supone un 0.0%
In [503]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[503]:
# Tot % Tot
False 479806 99.498573
True 2418 0.501427
msf_pressurecomplaint__c: queja por presión telemarketing.
Se puede observar que toma el valor de falso en casi todos los casos, se incluirá para analizarla.
Analsis de distribución por variables
-> msf_recencydonorcont__c: Variable numerica
In [504]:
# Vamos a realizar analisis por cada variable
var = "msf_recencydonorcont__c"
In [505]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_recencydonorcont__c es 301314. Lo que supone un 62.48423968943894%
El nº de vacios para la variable msf_recencydonorcont__c es 0. Lo que supone un 0.0%
Out[505]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_recencydonorcont__c']
In [506]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[506]:
# Tot % Tot
218.0 6014 3.324305
128.0 5548 3.066718
1102.0 4623 2.555414
4.0 3227 1.783760
583.0 3014 1.666022
... ... ...
10389.0 1 0.000553
6575.0 1 0.000553
4871.0 1 0.000553
2516.0 1 0.000553
5105.0 1 0.000553

6531 rows × 2 columns

msf_recencydonorcont__c: numero de dias desde el ultimo donativo.
Se puede observar que al tener muchos de los donantes recurrentes no hacer donanciones puntuales pues tienen el registro a nulo.
Analsis de distribución por variables
-> msf_recencyrecurringdonorcont__c: Variable numerica
In [507]:
# Vamos a realizar analisis por cada variable
var = "msf_recencyrecurringdonorcont__c"
In [508]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_recencyrecurringdonorcont__c es 868. Lo que supone un 0.17999933640797636%
El nº de vacios para la variable msf_recencyrecurringdonorcont__c es 0. Lo que supone un 0.0%
In [509]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[509]:
# Tot % Tot
4.0 391165 81.263140
36.0 18626 3.869485
66.0 17367 3.607933
156.0 9823 2.040693
186.0 9781 2.031968
128.0 7009 1.456095
218.0 6613 1.373827
95.0 5744 1.193296
247.0 4378 0.909514
340.0 4108 0.853422
277.0 3628 0.753704
309.0 2675 0.555722
583.0 25 0.005194
401.0 23 0.004778
550.0 23 0.004778
368.0 22 0.004570
462.0 21 0.004363
644.0 20 0.004155
704.0 16 0.003324
493.0 15 0.003116
431.0 12 0.002493
521.0 11 0.002285
612.0 11 0.002285
674.0 11 0.002285
2012.0 7 0.001454
1069.0 6 0.001246
1283.0 6 0.001246
1251.0 6 0.001246
1618.0 6 0.001246
5.0 6 0.001246
1102.0 5 0.001039
1192.0 5 0.001039
1678.0 5 0.001039
1009.0 5 0.001039
1922.0 4 0.000831
1342.0 4 0.000831
976.0 4 0.000831
1437.0 4 0.000831
2196.0 3 0.000623
1863.0 3 0.000623
2501.0 3 0.000623
1832.0 3 0.000623
2867.0 3 0.000623
1040.0 3 0.000623
1375.0 3 0.000623
1131.0 3 0.000623
2042.0 3 0.000623
736.0 3 0.000623
2105.0 3 0.000623
1223.0 3 0.000623
3474.0 3 0.000623
1496.0 3 0.000623
2804.0 3 0.000623
1802.0 3 0.000623
2347.0 2 0.000415
2987.0 2 0.000415
1161.0 2 0.000415
2439.0 2 0.000415
2378.0 2 0.000415
914.0 2 0.000415
2469.0 2 0.000415
1648.0 2 0.000415
3505.0 2 0.000415
3566.0 2 0.000415
3869.0 2 0.000415
2287.0 2 0.000415
1740.0 2 0.000415
948.0 2 0.000415
2714.0 2 0.000415
3414.0 2 0.000415
2258.0 2 0.000415
3687.0 2 0.000415
1559.0 2 0.000415
37.0 2 0.000415
766.0 2 0.000415
2685.0 2 0.000415
2074.0 2 0.000415
1769.0 2 0.000415
3351.0 2 0.000415
2167.0 2 0.000415
2623.0 1 0.000208
3627.0 1 0.000208
2532.0 1 0.000208
2990.0 1 0.000208
3320.0 1 0.000208
4569.0 1 0.000208
1894.0 1 0.000208
1314.0 1 0.000208
3293.0 1 0.000208
1709.0 1 0.000208
6305.0 1 0.000208
3140.0 1 0.000208
3659.0 1 0.000208
3442.0 1 0.000208
4755.0 1 0.000208
886.0 1 0.000208
2593.0 1 0.000208
5210.0 1 0.000208
4083.0 1 0.000208
3960.0 1 0.000208
858.0 1 0.000208
5819.0 1 0.000208
1590.0 1 0.000208
2654.0 1 0.000208
2563.0 1 0.000208
5268.0 1 0.000208
4933.0 1 0.000208
1955.0 1 0.000208
2136.0 1 0.000208
795.0 1 0.000208
1528.0 1 0.000208
1771.0 1 0.000208
219.0 1 0.000208
1892.0 1 0.000208
4814.0 1 0.000208
2775.0 1 0.000208
4358.0 1 0.000208
4146.0 1 0.000208
3749.0 1 0.000208
5057.0 1 0.000208
3416.0 1 0.000208
2837.0 1 0.000208
3839.0 1 0.000208
4512.0 1 0.000208
1468.0 1 0.000208
4723.0 1 0.000208
6245.0 1 0.000208
2410.0 1 0.000208
4784.0 1 0.000208
3078.0 1 0.000208
1983.0 1 0.000208
3050.0 1 0.000208
4877.0 1 0.000208
3781.0 1 0.000208
5027.0 1 0.000208
4295.0 1 0.000208
msf_recencyrecurringdonorcont__c: numero de dias desde la ultima aportacion de socio recurrente.
Casi no tiene nulos se analizará su inclusion en el modelo.
Analsis de distribución por variables
-> msf_recencytotalcont__c: Variable numerica
In [510]:
# Vamos a realizar analisis por cada variable
var = "msf_recencytotalcont__c"
In [511]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_recencytotalcont__c es 825. Lo que supone un 0.17108231859053055%
El nº de vacios para la variable msf_recencytotalcont__c es 0. Lo que supone un 0.0%
In [512]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[512]:
# Tot % Tot
4.0 391902 81.408977
36.0 18702 3.884927
66.0 17452 3.625267
156.0 9217 1.914628
186.0 8988 1.867058
128.0 7433 1.544041
218.0 6244 1.297053
95.0 5745 1.193397
247.0 3905 0.811177
340.0 3683 0.765062
277.0 3285 0.682386
309.0 2420 0.502702
127.0 331 0.068758
150.0 151 0.031367
149.0 132 0.027420
198.0 110 0.022850
151.0 106 0.022019
148.0 73 0.015164
152.0 60 0.012464
145.0 48 0.009971
143.0 33 0.006855
24.0 30 0.006232
3.0 29 0.006024
147.0 29 0.006024
146.0 27 0.005609
144.0 24 0.004985
142.0 23 0.004778
401.0 22 0.004570
5.0 22 0.004570
19.0 22 0.004570
78.0 21 0.004362
25.0 20 0.004155
368.0 20 0.004155
550.0 19 0.003947
583.0 19 0.003947
462.0 19 0.003947
23.0 18 0.003739
26.0 18 0.003739
644.0 18 0.003739
39.0 16 0.003324
18.0 16 0.003324
141.0 16 0.003324
704.0 15 0.003116
22.0 15 0.003116
138.0 14 0.002908
2.0 14 0.002908
493.0 14 0.002908
40.0 13 0.002700
17.0 12 0.002493
674.0 12 0.002493
12.0 12 0.002493
75.0 12 0.002493
521.0 12 0.002493
431.0 11 0.002285
99.0 11 0.002285
29.0 11 0.002285
612.0 11 0.002285
11.0 10 0.002077
96.0 9 0.001870
1102.0 8 0.001662
193.0 8 0.001662
15.0 8 0.001662
137.0 8 0.001662
38.0 8 0.001662
10.0 8 0.001662
194.0 8 0.001662
37.0 8 0.001662
184.0 7 0.001454
85.0 7 0.001454
242.0 7 0.001454
190.0 7 0.001454
134.0 7 0.001454
8.0 7 0.001454
16.0 7 0.001454
77.0 7 0.001454
197.0 7 0.001454
191.0 7 0.001454
74.0 6 0.001246
1283.0 6 0.001246
87.0 6 0.001246
1618.0 6 0.001246
192.0 6 0.001246
30.0 6 0.001246
1069.0 6 0.001246
243.0 6 0.001246
71.0 6 0.001246
1251.0 6 0.001246
136.0 6 0.001246
187.0 6 0.001246
189.0 6 0.001246
89.0 5 0.001039
140.0 5 0.001039
70.0 5 0.001039
1678.0 5 0.001039
139.0 5 0.001039
199.0 5 0.001039
27.0 5 0.001039
213.0 5 0.001039
1.0 5 0.001039
94.0 5 0.001039
204.0 5 0.001039
88.0 5 0.001039
123.0 5 0.001039
179.0 4 0.000831
47.0 4 0.000831
43.0 4 0.000831
211.0 4 0.000831
1192.0 4 0.000831
100.0 4 0.000831
135.0 4 0.000831
1040.0 4 0.000831
130.0 4 0.000831
1342.0 4 0.000831
64.0 4 0.000831
58.0 4 0.000831
68.0 4 0.000831
65.0 4 0.000831
219.0 4 0.000831
222.0 4 0.000831
67.0 4 0.000831
2012.0 4 0.000831
9.0 4 0.000831
1009.0 4 0.000831
205.0 3 0.000623
2501.0 3 0.000623
1922.0 3 0.000623
217.0 3 0.000623
1132.0 3 0.000623
117.0 3 0.000623
976.0 3 0.000623
176.0 3 0.000623
736.0 3 0.000623
180.0 3 0.000623
112.0 3 0.000623
2804.0 3 0.000623
304.0 3 0.000623
208.0 3 0.000623
13.0 3 0.000623
201.0 3 0.000623
1437.0 3 0.000623
129.0 3 0.000623
1496.0 3 0.000623
164.0 3 0.000623
1131.0 3 0.000623
200.0 3 0.000623
32.0 3 0.000623
131.0 3 0.000623
1223.0 3 0.000623
14.0 3 0.000623
20.0 3 0.000623
2196.0 3 0.000623
174.0 3 0.000623
225.0 3 0.000623
101.0 3 0.000623
2439.0 2 0.000415
2167.0 2 0.000415
1863.0 2 0.000415
183.0 2 0.000415
1740.0 2 0.000415
2685.0 2 0.000415
73.0 2 0.000415
216.0 2 0.000415
3414.0 2 0.000415
196.0 2 0.000415
116.0 2 0.000415
46.0 2 0.000415
122.0 2 0.000415
2105.0 2 0.000415
3869.0 2 0.000415
914.0 2 0.000415
80.0 2 0.000415
42.0 2 0.000415
102.0 2 0.000415
35.0 2 0.000415
519.0 2 0.000415
202.0 2 0.000415
215.0 2 0.000415
166.0 2 0.000415
1648.0 2 0.000415
124.0 2 0.000415
3505.0 2 0.000415
210.0 2 0.000415
2347.0 2 0.000415
234.0 2 0.000415
33.0 2 0.000415
1161.0 2 0.000415
3687.0 2 0.000415
2378.0 2 0.000415
203.0 2 0.000415
766.0 2 0.000415
61.0 2 0.000415
86.0 2 0.000415
28.0 2 0.000415
206.0 2 0.000415
1071.0 2 0.000415
3351.0 2 0.000415
93.0 2 0.000415
195.0 2 0.000415
21.0 2 0.000415
81.0 2 0.000415
261.0 2 0.000415
31.0 2 0.000415
1802.0 2 0.000415
57.0 2 0.000415
3474.0 2 0.000415
240.0 2 0.000415
44.0 2 0.000415
1559.0 2 0.000415
132.0 2 0.000415
98.0 2 0.000415
275.0 2 0.000415
6.0 2 0.000415
178.0 2 0.000415
185.0 2 0.000415
293.0 2 0.000415
269.0 2 0.000415
172.0 2 0.000415
173.0 2 0.000415
2714.0 2 0.000415
1375.0 2 0.000415
221.0 2 0.000415
227.0 2 0.000415
165.0 2 0.000415
2867.0 2 0.000415
76.0 2 0.000415
2469.0 1 0.000208
2987.0 1 0.000208
566.0 1 0.000208
590.0 1 0.000208
246.0 1 0.000208
1894.0 1 0.000208
155.0 1 0.000208
153.0 1 0.000208
264.0 1 0.000208
2532.0 1 0.000208
103.0 1 0.000208
7.0 1 0.000208
4083.0 1 0.000208
3960.0 1 0.000208
407.0 1 0.000208
1955.0 1 0.000208
162.0 1 0.000208
5210.0 1 0.000208
1173.0 1 0.000208
3293.0 1 0.000208
1709.0 1 0.000208
858.0 1 0.000208
253.0 1 0.000208
522.0 1 0.000208
4569.0 1 0.000208
3320.0 1 0.000208
1769.0 1 0.000208
2623.0 1 0.000208
2990.0 1 0.000208
119.0 1 0.000208
157.0 1 0.000208
3659.0 1 0.000208
3442.0 1 0.000208
229.0 1 0.000208
280.0 1 0.000208
486.0 1 0.000208
4755.0 1 0.000208
528.0 1 0.000208
52.0 1 0.000208
948.0 1 0.000208
274.0 1 0.000208
5268.0 1 0.000208
886.0 1 0.000208
1284.0 1 0.000208
4933.0 1 0.000208
113.0 1 0.000208
489.0 1 0.000208
257.0 1 0.000208
3190.0 1 0.000208
3627.0 1 0.000208
72.0 1 0.000208
41.0 1 0.000208
3140.0 1 0.000208
5819.0 1 0.000208
34.0 1 0.000208
526.0 1 0.000208
231.0 1 0.000208
922.0 1 0.000208
284.0 1 0.000208
154.0 1 0.000208
1314.0 1 0.000208
5027.0 1 0.000208
171.0 1 0.000208
2042.0 1 0.000208
1468.0 1 0.000208
163.0 1 0.000208
4723.0 1 0.000208
484.0 1 0.000208
2031.0 1 0.000208
302.0 1 0.000208
79.0 1 0.000208
2530.0 1 0.000208
1832.0 1 0.000208
159.0 1 0.000208
1528.0 1 0.000208
133.0 1 0.000208
339.0 1 0.000208
4784.0 1 0.000208
563.0 1 0.000208
4512.0 1 0.000208
233.0 1 0.000208
1108.0 1 0.000208
497.0 1 0.000208
308.0 1 0.000208
3839.0 1 0.000208
494.0 1 0.000208
1590.0 1 0.000208
5057.0 1 0.000208
3749.0 1 0.000208
4146.0 1 0.000208
54.0 1 0.000208
53.0 1 0.000208
4358.0 1 0.000208
1892.0 1 0.000208
479.0 1 0.000208
177.0 1 0.000208
121.0 1 0.000208
2136.0 1 0.000208
175.0 1 0.000208
2074.0 1 0.000208
244.0 1 0.000208
188.0 1 0.000208
552.0 1 0.000208
3781.0 1 0.000208
2775.0 1 0.000208
383.0 1 0.000208
170.0 1 0.000208
3416.0 1 0.000208
2654.0 1 0.000208
3566.0 1 0.000208
6305.0 1 0.000208
3506.0 1 0.000208
2258.0 1 0.000208
795.0 1 0.000208
90.0 1 0.000208
239.0 1 0.000208
4877.0 1 0.000208
169.0 1 0.000208
235.0 1 0.000208
266.0 1 0.000208
115.0 1 0.000208
82.0 1 0.000208
167.0 1 0.000208
1983.0 1 0.000208
59.0 1 0.000208
97.0 1 0.000208
181.0 1 0.000208
106.0 1 0.000208
126.0 1 0.000208
3078.0 1 0.000208
120.0 1 0.000208
msf_recencytotalcont__c: numero de dias desde la ultima aportacion.
Se puede observar que tiene un casi no tiene nulos, se analizará su inclusión como variable input al modelo.
Analsis de distribución por variables
-> msf_recencytotalscore__c: Variable numerica
In [513]:
# Vamos a realizar analisis por cada variable
var = "msf_recencytotalscore__c"
In [514]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_recencytotalscore__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_recencytotalscore__c es 0. Lo que supone un 0.0%
In [515]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[515]:
# Tot % Tot
5.0 480989 99.743895
0.0 825 0.171082
4.0 209 0.043341
3.0 106 0.021981
2.0 70 0.014516
1.0 25 0.005184
msf_recencytotalscore__c: numero de dias desde la ultima aportacion (punt)
Se puede observar que tiene no tiene nulos, se analizará su inclusión como variable input al modelo.
Analsis de distribución por variables
-> msf_PercomsSummary__c: Variable categorica
In [516]:
# Vamos a realizar analisis por cada variable
var = "msf_percomssummary__c"
In [517]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_percomssummary__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_percomssummary__c es 0. Lo que supone un 0.0%
In [518]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[518]:
# Tot % Tot
Todo 346210 71.794436
Varios 103631 21.490220
No captación de fondos 24698 5.121686
Nada 7677 1.591999
Sólo certificado fiscal 8 0.001659
msf_percomssummary__c: permiso de comunicación.
Se puede observar que no hay vacios. Se trabajará con aquellos que tienen o la variable informada a Todo o a Varios.
Analsis de distribución por variables
-> msf_scoringrfvdonor__c: Variable numerica
In [519]:
# Vamos a realizar analisis por cada variable
var = "msf_scoringrfvdonor__c"
In [520]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_scoringrfvdonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_scoringrfvdonor__c es 0. Lo que supone un 0.0%
In [521]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[521]:
# Tot % Tot
0.0 301314 62.484240
1.4 19513 4.046460
1.8 17403 3.608904
1.2 15442 3.202246
1.6 12404 2.572249
2.3 10131 2.100891
1.7 9557 1.981859
1.0 9392 1.947643
1.5 9153 1.898081
1.9 8442 1.750639
2.1 6815 1.413244
2.8 5418 1.123544
2.2 5227 1.083936
2.6 4299 0.891494
2.4 4194 0.869720
2.0 4192 0.869306
3.8 2895 0.600343
3.3 2873 0.595781
2.5 2777 0.575873
3.0 2717 0.563431
3.2 2685 0.556795
4.1 2662 0.552026
3.6 2536 0.525897
2.7 2439 0.505782
2.9 2009 0.416611
3.4 1962 0.406865
3.1 1807 0.374722
3.9 1783 0.369745
3.5 1711 0.354814
3.7 1395 0.289285
4.4 1217 0.252372
4.2 749 0.155322
4.3 705 0.146198
4.0 655 0.135829
4.6 625 0.129608
4.7 468 0.097050
4.9 402 0.083364
4.8 364 0.075484
1.3 307 0.063663
5.1 279 0.057857
4.5 275 0.057027
5.0 235 0.048733
5.2 171 0.035461
5.4 128 0.026544
5.5 107 0.022189
6.0 73 0.015138
5.7 62 0.012857
5.3 58 0.012028
5.6 52 0.010783
5.9 42 0.008710
6.5 23 0.004770
5.8 23 0.004770
0.8 13 0.002696
6.2 10 0.002074
0.4 7 0.001452
0.6 6 0.001244
6.1 4 0.000829
6.7 3 0.000622
6.4 3 0.000622
1.1 2 0.000415
0.5 2 0.000415
0.7 2 0.000415
6.3 2 0.000415
7.0 1 0.000207
6.8 1 0.000207
0.2 1 0.000207
msf_scoringrfvdonor__c: scoring donante.
No hay vacios, pero se va a priorizar los dos siguientes scoring.
Analsis de distribución por variables
-> msf_scoringrfvrecurringdonor__c: Variable numerica
In [522]:
# Vamos a realizar analisis por cada variable
var = "msf_scoringrfvrecurringdonor__c"
In [523]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_scoringrfvrecurringdonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_scoringrfvrecurringdonor__c es 0. Lo que supone un 0.0%
In [524]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[524]:
# Tot % Tot
5.0 131138 27.194416
4.5 97173 20.151009
3.5 76494 15.862753
3.0 38252 7.932413
4.7 20535 4.258394
4.2 18389 3.813373
2.0 15835 3.283744
3.2 13232 2.743953
5.5 11441 2.372549
4.0 11273 2.337710
4.4 9403 1.949924
3.9 8215 1.703565
2.9 5830 1.208982
1.5 5362 1.111931
2.7 5153 1.068591
2.4 2281 0.473017
6.0 2103 0.436104
3.7 1697 0.351911
3.6 1357 0.281404
4.1 1343 0.278501
2.5 1166 0.241796
2.6 1061 0.220022
3.4 753 0.156151
5.2 752 0.155944
2.1 556 0.115299
4.9 371 0.076935
1.0 210 0.043548
3.1 146 0.030276
5.7 143 0.029654
6.5 92 0.019078
4.6 75 0.015553
5.4 67 0.013894
0.5 55 0.011405
2.8 45 0.009332
3.3 31 0.006429
4.3 25 0.005184
1.8 19 0.003940
3.8 18 0.003733
1.6 17 0.003525
2.2 17 0.003525
1.9 15 0.003111
1.3 14 0.002903
1.4 12 0.002488
5.1 12 0.002488
0.0 9 0.001866
6.2 6 0.001244
2.3 6 0.001244
1.2 6 0.001244
4.8 4 0.000829
5.9 3 0.000622
0.9 3 0.000622
7.0 2 0.000415
5.6 2 0.000415
1.7 2 0.000415
6.1 1 0.000207
1.1 1 0.000207
6.7 1 0.000207
msf_scoringrfvrecurringdonor__c: scoring donante recurrente.
Se puede observar que no hay vacios. Hay buena distribucion y puede ser buena variable, se incluirá en e modelo, pero deberia poder ser dinamica, teniendo una cada año al menos en funcion de su evolucion.
Analsis de distribución por variables
-> msf_scoringrvtotal__c: Variable numerica
In [525]:
# Vamos a realizar analisis por cada variable
var = "msf_scoringrvtotal__c"
In [526]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_scoringrvtotal__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_scoringrvtotal__c es 0. Lo que supone un 0.0%
In [527]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[527]:
# Tot % Tot
5.0 182563 37.858547
4.2 139953 29.022405
2.6 91942 19.066243
1.8 31583 6.549446
5.8 16834 3.490909
3.4 13939 2.890565
6.6 3844 0.797140
7.4 320 0.066359
3.2 307 0.063663
4.0 284 0.058894
1.6 281 0.058272
2.4 69 0.014309
0.8 55 0.011405
3.8 48 0.009954
8.2 36 0.007465
2.2 34 0.007051
3.6 34 0.007051
4.8 30 0.006221
4.4 19 0.003940
4.6 17 0.003525
2.0 12 0.002488
0.0 7 0.001452
1.2 5 0.001037
1.4 4 0.000829
3.0 3 0.000622
7.2 1 0.000207
msf_scoringrvtotal__c: scoring total.
Se puede observar que no hay vacios. Hay buena distribucion y puede ser buena variable, se incluirá en e modelo, pero deberia poder ser dinamica, teniendo una cada año al menos en funcion de su evolucion.
Analsis de distribución por variables
-> msf_mailingsegment__c: Variable categorica
In [528]:
# Vamos a realizar analisis por cada variable
var = "msf_mailingsegment__c"
In [529]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_mailingsegment__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_mailingsegment__c es 7. Lo que supone un 0.0014516075516772288%
In [530]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[530]:
# Tot % Tot
SOC NO REC SIN EXTRA 313670 65.046534
SOC CON EXTRA ACT 47969 9.947452
SOC CON EXTRA NO REC 38684 8.021998
SOC NUEVOS 29419 6.100692
SOC CON EXTRA REC 28788 5.969840
SOC REC SIN EXTRA 21232 4.402933
EMPRESAS SOCIAS 2381 0.493754
No cumple ninguno de los criterios anteriores 68 0.014101
7 0.001452
BAJAS ANTIGUAS 2 0.000415
BAJAS ACT 2 0.000415
DON PS ACT 1 0.000207
DON UNICO REC 1 0.000207
msf_mailingsegment__c: segmento colaborador.
Se puede observar que casi no existen los vacios.
Analsis de distribución por variables
-> msf_membertype__c: Variable categorica
In [531]:
# Vamos a realizar analisis por cada variable
var = "msf_membertype__c"
In [532]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_membertype__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_membertype__c es 0. Lo que supone un 0.0%
In [533]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[533]:
# Tot % Tot
Socio 301335 62.488595
Socio + Exdonante 132714 27.521235
Socio + Donante 48175 9.990171
msf_membertype__c: tipo de miembro.
Se puede observar que no hay vacios, pero se descarta al estar ya centrados en socios por la variable es activo
Analsis de distribución por variables
-> npo02__totaloppamount__c: Variable numerica
In [534]:
# Vamos a realizar analisis por cada variable
var = "npo02__totaloppamount__c"
In [535]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__totaloppamount__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__totaloppamount__c es 0. Lo que supone un 0.0%
In [536]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[536]:
# Tot % Tot
60.00 2118 0.439215
120.00 1991 0.412879
600.00 1773 0.367671
300.00 1769 0.366842
240.00 1660 0.344238
... ... ...
3710.16 1 0.000207
2941.45 1 0.000207
426.21 1 0.000207
10741.52 1 0.000207
1628.70 1 0.000207

58885 rows × 2 columns

npo02__totaloppamount__c: total donado.
Se puede observar que esta variable no tiene registros a nulo. Se analizará la inclusión en el modelo.
Analsis de distribución por variables
-> npo02__oppamountthisyear__c: Variable numerica
In [537]:
# Vamos a realizar analisis por cada variable
var = "npo02__oppamountthisyear__c"
In [538]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__oppamountthisyear__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__oppamountthisyear__c es 0. Lo que supone un 0.0%
In [539]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[539]:
# Tot % Tot
0.0 482224 100.0
npo02__OppAmountThisYear__c: importe total de aportaciones al año que realizó este año.
Se puede observar que no hay vacios. Pero todos los valores e informan a 0.
Analsis de distribución por variables
-> npo02__oppamount2yearsago__c: Variable numerica
In [540]:
# Vamos a realizar analisis por cada variable
var = "npo02__oppamount2yearsago__c"
In [541]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__oppamount2yearsago__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__oppamount2yearsago__c es 0. Lo que supone un 0.0%
In [542]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[542]:
# Tot % Tot
0.0 482224 100.0
npo02__oppamount2yearsago__c: importe total de aportaciones al año que realizó hace 2 años.
Se puede observar que no hay vacios. Pero todos los valores e informan a 0.
Analsis de distribución por variables
-> npo02__oppamountlastyear__c: Variable numerica
In [543]:
# Vamos a realizar analisis por cada variable
var = "npo02__oppamountlastyear__c"
In [544]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__oppamountlastyear__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__oppamountlastyear__c es 0. Lo que supone un 0.0%
In [545]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[545]:
# Tot % Tot
0.0 482224 100.0
npo02__oppamountlastyear__c: importe total de aportaciones al año que realizó el año pasado.
Se puede observar que no hay vacios. Pero todos los valores e informan a 0.
Analsis de distribución por variables
-> npo02__best_gift_year_total__c: Variable numerica
In [546]:
# Vamos a realizar analisis por cada variable
var = "npo02__best_gift_year_total__c"
In [547]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__best_gift_year_total__c es 825. Lo que supone un 0.17108231859053055%
El nº de vacios para la variable npo02__best_gift_year_total__c es 0. Lo que supone un 0.0%
In [548]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[548]:
# Tot % Tot
120.00 56389 11.713568
180.00 34896 7.248873
240.00 24252 5.037817
60.00 24010 4.987547
144.00 15062 3.128798
... ... ...
8013.95 1 0.000208
4080.00 1 0.000208
569.00 1 0.000208
4074.00 1 0.000208
161.01 1 0.000208

5063 rows × 2 columns

npo02__best_gift_year_total__c: importe total de aportaciones al año que más ha aportado.
Se puede observar que esta variable no tiene registros a nulo. Como es información parecida a la recogida en otras variables no se tendrá en cuenta.
Analsis de distribución por variables
-> msf_totalfiscaloppamount__c: Variable numerica
In [549]:
# Vamos a realizar analisis por cada variable
var = "msf_totalfiscaloppamount__c"
In [550]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_totalfiscaloppamount__c es 3. Lo que supone un 0.0006221175221473838%
El nº de vacios para la variable msf_totalfiscaloppamount__c es 0. Lo que supone un 0.0%
In [551]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[551]:
# Tot % Tot
60.00 2118 0.439218
120.00 1990 0.412674
600.00 1773 0.367674
300.00 1769 0.366844
240.00 1660 0.344241
... ... ...
4777.66 1 0.000207
10743.15 1 0.000207
1301.30 1 0.000207
5800.98 1 0.000207
1628.70 1 0.000207

58918 rows × 2 columns

msf_totalfiscaloppamount__c: importe total de aportaciones fiscal cobradas.
Se puede observar que esta variable casi no tiene registros a nulo. Se analizará la inclusión en el modelo.
Analsis de distribución por variables
-> msf_lastannualizedquota__c: Variable numerica
In [552]:
# Vamos a realizar analisis por cada variable
var = "msf_lastannualizedquota__c"
In [553]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_lastannualizedquota__c es 1. Lo que supone un 0.0002073725073824613%
El nº de vacios para la variable msf_lastannualizedquota__c es 0. Lo que supone un 0.0%
In [554]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[554]:
# Tot % Tot
120.00 86866 18.013658
180.00 52333 10.852448
240.00 38002 7.880586
60.00 37911 7.861715
144.00 26221 5.437526
300.00 16044 3.327091
72.00 15616 3.238336
360.00 15207 3.153520
168.00 10851 2.250204
96.00 10022 2.078292
84.00 9342 1.937278
100.00 9122 1.891656
36.00 8783 1.821357
204.00 5975 1.239053
200.00 5333 1.105920
600.00 5142 1.066312
50.00 5118 1.061335
480.00 5113 1.060298
80.00 4653 0.964906
216.00 4515 0.936289
150.00 4346 0.901243
192.00 4177 0.866197
420.00 4119 0.854169
156.00 3710 0.769354
30.00 3646 0.756082
108.00 3585 0.743432
20.00 3489 0.723524
312.00 3444 0.714192
40.00 3368 0.698432
132.00 3262 0.676451
264.00 3250 0.673962
228.00 2926 0.606773
72.12 2852 0.591428
160.00 2425 0.502879
276.00 2165 0.448962
720.00 1983 0.411221
12.00 1834 0.380322
48.00 1829 0.379285
140.00 1773 0.367672
90.00 1647 0.341543
10.00 1570 0.325576
70.00 1511 0.313341
51.96 1493 0.309608
15.00 1420 0.294470
384.00 1339 0.277672
400.00 1332 0.276221
540.00 1332 0.276221
60.10 1132 0.234746
1200.00 1131 0.234539
288.00 1115 0.231221
120.20 1099 0.227903
75.00 1097 0.227488
336.00 1014 0.210276
250.00 981 0.203433
324.00 978 0.202811
25.00 942 0.195345
252.00 855 0.177304
260.00 849 0.176060
30.05 809 0.167765
144.24 675 0.139977
396.00 638 0.132304
3.00 602 0.124839
130.00 601 0.124631
110.00 589 0.122143
360.60 576 0.119447
280.00 575 0.119239
24.00 547 0.113433
500.00 534 0.110737
220.00 529 0.109700
660.00 504 0.104516
840.00 498 0.103272
125.00 497 0.103064
320.00 481 0.099746
216.36 446 0.092488
5.00 436 0.090415
45.00 412 0.085438
35.00 407 0.084401
90.15 372 0.077143
170.00 336 0.069677
900.00 335 0.069470
65.00 335 0.069470
960.00 333 0.069055
240.40 331 0.068640
408.00 325 0.067396
432.00 307 0.063663
88.00 302 0.062627
780.00 290 0.060138
210.00 289 0.059931
18.00 279 0.057857
350.00 272 0.056405
444.00 262 0.054332
32.00 255 0.052880
1000.00 246 0.051014
175.00 244 0.050599
504.00 244 0.050599
55.00 240 0.049770
624.00 240 0.049770
372.00 238 0.049355
800.00 215 0.044585
165.00 212 0.043963
348.00 210 0.043548
230.00 207 0.042926
85.00 204 0.042304
42.00 200 0.041475
456.00 195 0.040438
18.03 193 0.040023
1080.00 190 0.039401
520.00 186 0.038571
52.00 181 0.037535
28.00 181 0.037535
22.00 177 0.036705
56.00 161 0.033387
112.00 158 0.032765
1800.00 148 0.030691
92.00 144 0.029862
105.00 140 0.029032
48.08 135 0.027995
340.00 127 0.026336
150.25 126 0.026129
152.00 126 0.026129
721.20 125 0.025922
516.00 122 0.025299
440.00 121 0.025092
528.00 121 0.025092
135.00 116 0.024055
68.00 115 0.023848
225.00 112 0.023226
6.00 112 0.023226
190.00 112 0.023226
104.00 111 0.023018
128.00 105 0.021774
2400.00 105 0.021774
224.00 104 0.021567
115.00 103 0.021359
64.00 102 0.021152
552.00 102 0.021152
124.00 96 0.019908
450.00 95 0.019700
270.00 94 0.019493
700.00 93 0.019286
62.00 90 0.018664
104.04 88 0.018249
148.00 86 0.017834
14.00 86 0.017834
864.00 86 0.017834
1440.00 85 0.017627
232.00 85 0.017627
492.00 84 0.017419
1500.00 83 0.017212
54.00 82 0.017005
95.00 81 0.016797
36.06 76 0.015760
380.00 76 0.015760
38.00 75 0.015553
1020.00 73 0.015138
16.00 72 0.014931
460.00 71 0.014723
108.12 70 0.014516
34.85 69 0.014309
468.00 68 0.014101
8.00 68 0.014101
184.00 67 0.013894
17.00 64 0.013272
76.00 63 0.013064
310.00 63 0.013064
24.04 62 0.012857
44.00 60 0.012442
66.00 59 0.012235
330.00 58 0.012028
164.00 58 0.012028
180.24 57 0.011820
180.30 56 0.011613
390.00 56 0.011613
155.00 55 0.011406
103.92 55 0.011406
136.00 55 0.011406
275.00 54 0.011198
576.00 53 0.010991
74.00 52 0.010783
96.16 52 0.010783
139.40 51 0.010576
21.00 51 0.010576
560.00 48 0.009954
1320.00 47 0.009747
300.50 45 0.009332
26.00 45 0.009332
288.48 43 0.008917
145.00 41 0.008502
550.00 41 0.008502
2000.00 40 0.008295
640.00 40 0.008295
208.00 40 0.008295
564.00 39 0.008088
116.00 38 0.007880
648.00 37 0.007673
33.00 37 0.007673
185.00 36 0.007465
27.00 36 0.007465
126.00 35 0.007258
212.00 35 0.007258
248.00 35 0.007258
93.15 34 0.007051
744.00 34 0.007051
620.00 34 0.007051
290.00 34 0.007051
172.00 33 0.006843
82.00 33 0.006843
364.00 33 0.006843
78.00 33 0.006843
63.00 32 0.006636
3000.00 32 0.006636
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268.00 10 0.002074
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87.00 9 0.001866
880.00 9 0.001866
768.00 9 0.001866
580.00 9 0.001866
284.00 9 0.001866
84.12 9 0.001866
78.13 9 0.001866
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292.00 9 0.001866
344.00 8 0.001659
109.44 8 0.001659
316.00 8 0.001659
732.00 8 0.001659
81.00 8 0.001659
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198.00 8 0.001659
2040.00 8 0.001659
134.00 8 0.001659
470.00 8 0.001659
2160.00 8 0.001659
42.07 8 0.001659
1100.00 8 0.001659
115.36 8 0.001659
1400.00 8 0.001659
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430.00 7 0.001452
41.00 7 0.001452
106.00 7 0.001452
4800.00 7 0.001452
101.00 7 0.001452
804.00 7 0.001452
255.00 7 0.001452
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47.00 7 0.001452
222.00 7 0.001452
142.00 7 0.001452
57.00 7 0.001452
820.00 7 0.001452
2100.00 7 0.001452
58.00 7 0.001452
256.00 7 0.001452
29.00 6 0.001244
372.60 6 0.001244
182.00 6 0.001244
888.00 6 0.001244
296.00 6 0.001244
120.12 6 0.001244
332.00 6 0.001244
920.00 6 0.001244
202.00 6 0.001244
376.00 6 0.001244
96.12 6 0.001244
850.00 6 0.001244
328.00 6 0.001244
53.00 6 0.001244
57.69 6 0.001244
60.24 6 0.001244
1380.00 6 0.001244
816.00 6 0.001244
285.00 6 0.001244
984.00 5 0.001037
368.00 5 0.001037
852.00 5 0.001037
93.00 5 0.001037
7200.00 5 0.001037
154.00 5 0.001037
186.00 5 0.001037
123.00 5 0.001037
252.36 5 0.001037
1620.00 5 0.001037
132.20 5 0.001037
120.48 5 0.001037
1040.00 5 0.001037
19.00 5 0.001037
305.00 5 0.001037
91.00 5 0.001037
416.00 5 0.001037
1920.00 5 0.001037
308.00 5 0.001037
43.00 5 0.001037
57.68 5 0.001037
588.00 5 0.001037
245.00 5 0.001037
1803.00 5 0.001037
97.00 5 0.001037
625.00 4 0.000829
475.00 4 0.000829
45.07 4 0.000829
912.00 4 0.000829
40.05 4 0.000829
14.42 4 0.000829
194.00 4 0.000829
448.00 4 0.000829
108.16 4 0.000829
286.00 4 0.000829
5000.00 4 0.000829
300.51 4 0.000829
1202.00 4 0.000829
161.00 4 0.000829
180.36 4 0.000829
346.08 4 0.000829
388.00 4 0.000829
90.36 4 0.000829
182.40 4 0.000829
201.00 4 0.000829
118.00 4 0.000829
345.00 4 0.000829
740.00 4 0.000829
90.12 4 0.000829
352.00 4 0.000829
210.35 4 0.000829
265.00 3 0.000622
12000.00 3 0.000622
396.60 3 0.000622
149.00 3 0.000622
876.00 3 0.000622
1128.00 3 0.000622
30.12 3 0.000622
66.11 3 0.000622
166.00 3 0.000622
488.00 3 0.000622
18000.00 3 0.000622
996.00 3 0.000622
43.20 3 0.000622
315.00 3 0.000622
1120.00 3 0.000622
708.00 3 0.000622
234.00 3 0.000622
450.75 3 0.000622
50.40 3 0.000622
100.15 3 0.000622
510.00 3 0.000622
274.00 3 0.000622
151.00 3 0.000622
262.00 3 0.000622
924.00 3 0.000622
240.36 3 0.000622
187.00 3 0.000622
71.00 3 0.000622
35.05 3 0.000622
424.00 3 0.000622
1300.00 3 0.000622
828.00 3 0.000622
8.66 2 0.000415
158.00 2 0.000415
137.00 2 0.000415
36.66 2 0.000415
93.24 2 0.000415
189.00 2 0.000415
3606.00 2 0.000415
109.00 2 0.000415
725.00 2 0.000415
570.00 2 0.000415
412.00 2 0.000415
530.00 2 0.000415
860.00 2 0.000415
65.10 2 0.000415
1596.00 2 0.000415
171.96 2 0.000415
121.00 2 0.000415
49.00 2 0.000415
1104.00 2 0.000415
153.00 2 0.000415
113.00 2 0.000415
1224.00 2 0.000415
89.00 2 0.000415
17.32 2 0.000415
223.92 2 0.000415
1360.00 2 0.000415
1860.00 2 0.000415
1464.00 2 0.000415
3900.00 2 0.000415
1056.00 2 0.000415
69.00 2 0.000415
30.04 2 0.000415
146.00 2 0.000415
159.96 2 0.000415
4000.00 2 0.000415
294.00 2 0.000415
356.00 2 0.000415
282.00 2 0.000415
60.05 2 0.000415
1980.00 2 0.000415
111.00 2 0.000415
79.32 2 0.000415
446.00 2 0.000415
159.40 2 0.000415
536.00 2 0.000415
9.01 2 0.000415
1284.00 2 0.000415
127.00 2 0.000415
92.12 2 0.000415
295.00 2 0.000415
420.60 2 0.000415
525.00 2 0.000415
70.10 2 0.000415
346.00 2 0.000415
2163.60 2 0.000415
103.00 2 0.000415
42.05 2 0.000415
99.00 2 0.000415
306.00 2 0.000415
14.40 2 0.000415
218.00 2 0.000415
540.60 2 0.000415
177.00 2 0.000415
810.00 2 0.000415
86.52 2 0.000415
167.00 2 0.000415
366.00 2 0.000415
585.00 2 0.000415
2500.00 2 0.000415
173.00 2 0.000415
157.00 2 0.000415
139.36 1 0.000207
390.15 1 0.000207
438.00 1 0.000207
4200.00 1 0.000207
10.50 1 0.000207
2328.00 1 0.000207
300.20 1 0.000207
188.04 1 0.000207
2200.00 1 0.000207
17.34 1 0.000207
100.10 1 0.000207
675.00 1 0.000207
532.00 1 0.000207
812.00 1 0.000207
710.00 1 0.000207
280.40 1 0.000207
1356.00 1 0.000207
132.12 1 0.000207
728.00 1 0.000207
824.56 1 0.000207
79.20 1 0.000207
1803.03 1 0.000207
1250.00 1 0.000207
210.32 1 0.000207
164.40 1 0.000207
214.00 1 0.000207
669.60 1 0.000207
80.40 1 0.000207
240.12 1 0.000207
436.00 1 0.000207
212.76 1 0.000207
480.24 1 0.000207
171.00 1 0.000207
216.72 1 0.000207
322.00 1 0.000207
842.88 1 0.000207
722.88 1 0.000207
796.00 1 0.000207
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2016.00 1 0.000207
87.96 1 0.000207
133.32 1 0.000207
343.92 1 0.000207
225.35 1 0.000207
139.92 1 0.000207
206.00 1 0.000207
243.96 1 0.000207
30.40 1 0.000207
364.80 1 0.000207
2520.00 1 0.000207
180.10 1 0.000207
1644.00 1 0.000207
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60.12 1 0.000207
260.10 1 0.000207
247.68 1 0.000207
120.10 1 0.000207
90.14 1 0.000207
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338.00 1 0.000207
592.00 1 0.000207
258.00 1 0.000207
1110.00 1 0.000207
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138.15 1 0.000207
38.40 1 0.000207
462.00 1 0.000207
147.00 1 0.000207
12.50 1 0.000207
60.15 1 0.000207
558.00 1 0.000207
36.08 1 0.000207
2800.00 1 0.000207
14.02 1 0.000207
143.00 1 0.000207
123.96 1 0.000207
208.08 1 0.000207
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4080.00 1 0.000207
318.00 1 0.000207
323.00 1 0.000207
374.00 1 0.000207
3020.00 1 0.000207
333.00 1 0.000207
311.00 1 0.000207
272.12 1 0.000207
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302.88 1 0.000207
60.01 1 0.000207
126.84 1 0.000207
1092.00 1 0.000207
484.00 1 0.000207
36.05 1 0.000207
152.24 1 0.000207
550.75 1 0.000207
132.22 1 0.000207
4500.00 1 0.000207
385.00 1 0.000207
150.15 1 0.000207
798.00 1 0.000207
281.00 1 0.000207
238.00 1 0.000207
505.00 1 0.000207
147.12 1 0.000207
224.24 1 0.000207
156.24 1 0.000207
240.20 1 0.000207
70.01 1 0.000207
260.40 1 0.000207
2884.80 1 0.000207
105.10 1 0.000207
409.44 1 0.000207
264.24 1 0.000207
90.75 1 0.000207
362.00 1 0.000207
211.52 1 0.000207
252.40 1 0.000207
138.23 1 0.000207
3800.00 1 0.000207
336.56 1 0.000207
402.00 1 0.000207
131.00 1 0.000207
1202.02 1 0.000207
100.92 1 0.000207
32.05 1 0.000207
692.28 1 0.000207
40.06 1 0.000207
415.00 1 0.000207
175.25 1 0.000207
466.64 1 0.000207
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641.00 1 0.000207
245.16 1 0.000207
270.03 1 0.000207
565.00 1 0.000207
901.00 1 0.000207
168.24 1 0.000207
65.86 1 0.000207
93.72 1 0.000207
1442.43 1 0.000207
242.00 1 0.000207
841.40 1 0.000207
302.00 1 0.000207
290.10 1 0.000207
1204.00 1 0.000207
193.00 1 0.000207
59.00 1 0.000207
102.17 1 0.000207
162.05 1 0.000207
126.15 1 0.000207
108.24 1 0.000207
110.40 1 0.000207
3606.12 1 0.000207
300.48 1 0.000207
1502.53 1 0.000207
55.92 1 0.000207
2880.00 1 0.000207
544.00 1 0.000207
144.20 1 0.000207
1752.00 1 0.000207
71.96 1 0.000207
270.05 1 0.000207
901.20 1 0.000207
273.00 1 0.000207
340.08 1 0.000207
458.00 1 0.000207
90.10 1 0.000207
1750.00 1 0.000207
320.50 1 0.000207
19.50 1 0.000207
199.00 1 0.000207
278.00 1 0.000207
77.10 1 0.000207
40.20 1 0.000207
164.24 1 0.000207
148.24 1 0.000207
608.00 1 0.000207
117.00 1 0.000207
169.00 1 0.000207
160.20 1 0.000207
382.08 1 0.000207
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181.00 1 0.000207
289.00 1 0.000207
382.00 1 0.000207
590.00 1 0.000207
2280.00 1 0.000207
335.00 1 0.000207
2700.00 1 0.000207
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572.00 1 0.000207
241.00 1 0.000207
59.88 1 0.000207
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107.00 1 0.000207
428.00 1 0.000207
76.12 1 0.000207
1160.00 1 0.000207
198.36 1 0.000207
253.00 1 0.000207
972.00 1 0.000207
39.96 1 0.000207
720.80 1 0.000207
4201.00 1 0.000207
99.60 1 0.000207
1992.00 1 0.000207
101.96 1 0.000207
99.96 1 0.000207
2760.00 1 0.000207
793.32 1 0.000207
160.25 1 0.000207
63.60 1 0.000207
140.20 1 0.000207
8000.00 1 0.000207
1502.40 1 0.000207
35.88 1 0.000207
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158.64 1 0.000207
112.12 1 0.000207
55.56 1 0.000207
3180.00 1 0.000207
115.32 1 0.000207
240.10 1 0.000207
84.60 1 0.000207
480.60 1 0.000207
306.51 1 0.000207
468.12 1 0.000207
200.04 1 0.000207
4360.00 1 0.000207
75.72 1 0.000207
207.00 1 0.000207
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176.24 1 0.000207
3300.00 1 0.000207
199.20 1 0.000207
1044.00 1 0.000207
249.96 1 0.000207
277.00 1 0.000207
1280.00 1 0.000207
630.00 1 0.000207
451.00 1 0.000207
64.90 1 0.000207
464.00 1 0.000207
1116.00 1 0.000207
8400.00 1 0.000207
msf_lastannualizedquota__c: importe anualizado de la ultima cuota de socio.
Se puede observar que esta variable no tiene registros a nulo. Se analizará la inclusión en el modelo.
Analsis de distribución por variables
-> msf_valuetotalcont__c: Variable numerica
In [555]:
# Vamos a realizar analisis por cada variable
var = "msf_valuetotalcont__c"
In [556]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_valuetotalcont__c es 7. Lo que supone un 0.0014516075516772288%
El nº de vacios para la variable msf_valuetotalcont__c es 0. Lo que supone un 0.0%
In [557]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[557]:
# Tot % Tot
120.0 81612 16.924331
180.0 48292 10.014578
60.0 37037 7.680567
240.0 34408 7.135377
144.0 24709 5.124042
... ... ...
2670.0 1 0.000207
972.0 1 0.000207
921.0 1 0.000207
2170.0 1 0.000207
8400.0 1 0.000207

1493 rows × 2 columns

msf_valuetotalcont__c: valor colaborador.
Casi no tiene registros a nulo. msf_ValueTotalDesc__c
Analsis de distribución por variables
-> msf_ValueTotalDesc__c: Variable catwgorica
In [558]:
# Vamos a realizar analisis por cada variable
var = "msf_valuetotaldesc__c"
In [559]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_valuetotaldesc__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_valuetotaldesc__c es 0. Lo que supone un 0.0%
In [560]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[560]:
# Tot % Tot
Medio 182862 37.920551
Bajo 140387 29.112404
Muy bajo 137923 28.601438
Alto 20688 4.290122
Muy Alto 357 0.074032
Nulo 7 0.001452
msf_ValueTotalDesc__c: descriptivo valor colaborador.
Se puede observar que esta variable no tiene registros a nulo. Se analizará la inclusión en el modelo.
Analsis de distribución por variables
-> msf_valuedonorcont__c: Variable numerica
In [561]:
# Vamos a realizar analisis por cada variable
var = "msf_valuedonorcont__c"
In [562]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_valuedonorcont__c es 301547. Lo que supone un 62.53255748365905%
El nº de vacios para la variable msf_valuedonorcont__c es 0. Lo que supone un 0.0%
Out[562]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_recencydonorcont__c',
 'msf_valuedonorcont__c']
In [563]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[563]:
# Tot % Tot
30.00 18977 10.503274
50.00 17792 9.847407
100.00 17790 9.846300
60.00 17422 9.642622
20.00 16709 9.247995
... ... ...
365.12 1 0.000553
283.05 1 0.000553
1578.00 1 0.000553
321.00 1 0.000553
1.17 1 0.000553

1613 rows × 2 columns

msf_valuedonorcont__c: suma de las donaciones de los ultimoss 365 dias.
Se puede observar que hay un 62% de nulos. Se descarta como candidata.
Analsis de distribución por variables
-> msf_lastyeardonorvalue__c: Variable numerica
In [564]:
# Vamos a realizar analisis por cada variable
var = "msf_lastyeardonorvalue__c"
In [565]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_lastyeardonorvalue__c es 434203. Lo que supone un 90.04176482298682%
El nº de vacios para la variable msf_lastyeardonorvalue__c es 0. Lo que supone un 0.0%
Out[565]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_recencydonorcont__c',
 'msf_valuedonorcont__c',
 'msf_lastyeardonorvalue__c']
In [566]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[566]:
# Tot % Tot
100.00 5218 10.866079
50.00 5073 10.564128
20.00 4290 8.933592
30.00 3918 8.158930
60.00 3858 8.033985
10.00 2052 4.273131
200.00 1927 4.012828
40.00 1873 3.900377
1.00 1837 3.825410
150.00 1261 2.625934
300.00 1024 2.132400
120.00 960 1.999125
90.00 940 1.957477
25.00 852 1.774224
80.00 773 1.609712
15.00 711 1.480602
5.00 662 1.378564
250.00 598 1.245289
500.00 564 1.174486
70.00 403 0.839216
400.00 399 0.830886
125.00 392 0.816310
2.00 295 0.614315
1000.00 279 0.580996
110.00 242 0.503946
160.00 236 0.491452
600.00 231 0.481040
180.00 229 0.476875
45.00 224 0.466463
140.00 183 0.381083
130.00 171 0.356094
350.00 163 0.339435
39.00 160 0.333188
75.00 156 0.324858
66.00 140 0.291539
240.00 139 0.289457
450.00 127 0.264468
3.00 125 0.260303
55.00 101 0.210325
220.00 101 0.210325
16.00 101 0.210325
35.00 99 0.206160
170.00 94 0.195748
2000.00 93 0.193665
800.00 85 0.177006
190.00 83 0.172841
78.00 82 0.170759
175.00 81 0.168676
12.00 81 0.168676
26.00 80 0.166594
61.00 77 0.160347
270.00 75 0.156182
210.00 74 0.154099
700.00 71 0.147852
4.00 64 0.133275
550.00 64 0.133275
8.00 61 0.127028
51.00 61 0.127028
1500.00 60 0.124945
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msf_lastyeardonorvalue__c: suma de las aportaciones de los ultimos 365 dias.
Se puede observar que hay un 90% de nulos. Se descarta como candidata.
Analsis de distribución por variables
-> msf_maximumdonorvalue__c: Variable numerica
In [567]:
# Vamos a realizar analisis por cada variable
var = "msf_maximumdonorvalue__c"
In [568]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_maximumdonorvalue__c es 301370. Lo que supone un 62.49585254985235%
El nº de vacios para la variable msf_maximumdonorvalue__c es 0. Lo que supone un 0.0%
Out[568]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_recencydonorcont__c',
 'msf_valuedonorcont__c',
 'msf_lastyeardonorvalue__c',
 'msf_maximumdonorvalue__c']
In [569]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[569]:
# Tot % Tot
60.00 22402 12.386787
100.00 22381 12.375176
30.00 18983 10.496312
50.00 16425 9.081911
20.00 13282 7.344045
... ... ...
1502.00 1 0.000553
303.59 1 0.000553
249.01 1 0.000553
267.00 1 0.000553
1.17 1 0.000553

1500 rows × 2 columns

msf_maximumdonorvalue__c: importe más elevado de todos los donativos.
Se puede observar que existe un 62% de nulos. Se analizará la inclusión en el modelo.
Analsis de distribución por variables
-> msf_averagedonorvalue__c: Variable numerica
In [570]:
# Vamos a realizar analisis por cada variable
var = "msf_averagedonorvalue__c"
In [571]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_averagedonorvalue__c es 301370. Lo que supone un 62.49585254985235%
El nº de vacios para la variable msf_averagedonorvalue__c es 0. Lo que supone un 0.0%
Out[571]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_recencydonorcont__c',
 'msf_valuedonorcont__c',
 'msf_lastyeardonorvalue__c',
 'msf_maximumdonorvalue__c',
 'msf_averagedonorvalue__c']
In [572]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[572]:
# Tot % Tot
30.00 13617 7.529278
60.00 11141 6.160218
20.00 10202 5.641014
50.00 8951 4.949296
100.00 7915 4.376458
... ... ...
202.29 1 0.000553
282.86 1 0.000553
355.49 1 0.000553
92.40 1 0.000553
232.77 1 0.000553

14837 rows × 2 columns

msf_averagedonorvalue__c: importe medio de todos los donativos.
Se puede observar que tiene un 62% de nulos. Se analizará la inclusión en el modelo.
Analsis de distribución por variables
-> msf_lifetime__c: Variable numerica
In [573]:
# Vamos a realizar analisis por cada variable
var = "msf_lifetime__c"
In [574]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_lifetime__c es 825. Lo que supone un 0.17108231859053055%
El nº de vacios para la variable msf_lifetime__c es 0. Lo que supone un 0.0%
In [575]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[575]:
# Tot % Tot
8.0 37707 7.832796
7.0 37464 7.782318
6.0 35559 7.386596
5.0 28262 5.870806
0.0 24529 5.095357
9.0 24307 5.049242
4.0 22817 4.739727
3.0 20393 4.236195
11.0 19939 4.141886
10.0 19621 4.075829
12.0 18820 3.909439
1.0 18237 3.788334
13.0 17727 3.682392
14.0 16578 3.443713
2.0 16485 3.424394
18.0 14209 2.951606
17.0 13801 2.866853
16.0 12218 2.538019
19.0 11190 2.324475
15.0 10417 2.163901
28.0 9775 2.030540
20.0 8964 1.862073
23.0 7261 1.508312
22.0 5446 1.131286
24.0 5255 1.091610
29.0 5218 1.083924
21.0 3937 0.817825
25.0 3885 0.807023
30.0 3582 0.744081
27.0 3374 0.700874
26.0 3312 0.687995
31.0 667 0.138555
32.0 173 0.035937
34.0 133 0.027628
33.0 80 0.016618
35.0 43 0.008932
36.0 14 0.002908
msf_lifetime__c: numero de años enteros desde primera aportacion a la ultima.
Se puede observar que tiene un 0.17% de registros a vacio. Se analizará la inclusión en el modelo.
Analsis de distribución por variables
-> msf_commitment__c: Variable numerica
In [576]:
# Vamos a realizar analisis por cada variable
var = "msf_commitment__c"
In [577]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_commitment__c es 4048. Lo que supone un 0.8394439098842031%
El nº de vacios para la variable msf_commitment__c es 0. Lo que supone un 0.0%
In [578]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[578]:
# Tot % Tot
0.0 273596 57.216590
1.0 102301 21.394006
2.0 47492 9.931908
3.0 23798 4.976829
4.0 12718 2.659690
5.0 7030 1.470170
6.0 4175 0.873109
7.0 2481 0.518847
8.0 1512 0.316202
9.0 963 0.201390
10.0 583 0.121922
11.0 433 0.090552
12.0 268 0.056046
13.0 194 0.040571
14.0 132 0.027605
15.0 95 0.019867
16.0 93 0.019449
17.0 59 0.012339
18.0 44 0.009202
19.0 31 0.006483
20.0 26 0.005437
21.0 23 0.004810
22.0 19 0.003973
23.0 14 0.002928
25.0 10 0.002091
29.0 10 0.002091
26.0 8 0.001673
24.0 8 0.001673
28.0 8 0.001673
27.0 7 0.001464
32.0 7 0.001464
30.0 6 0.001255
31.0 6 0.001255
33.0 4 0.000837
34.0 3 0.000627
36.0 2 0.000418
42.0 2 0.000418
61.0 2 0.000418
38.0 2 0.000418
47.0 1 0.000209
43.0 1 0.000209
80.0 1 0.000209
57.0 1 0.000209
56.0 1 0.000209
37.0 1 0.000209
71.0 1 0.000209
35.0 1 0.000209
46.0 1 0.000209
45.0 1 0.000209
54.0 1 0.000209
msf_commitment__c: suma de iteraciones.
Se puede observar que tiene un 0.8% de nulos, pero el 57% tiene valor 0, por lo que se descarta la variable.

3.2. Tabla contactos que tienen o han tenido una donación recurrente¶

In [579]:
# Se analizar solo los contactos que tienen donaciones recurrentes, ya que son el objetivo del analisis, por ello solo se tendrán en cuanta los contactos coincidentes entre la tabla recurring donation y contactos
df_contactos_f = df_contactos[df_contactos.id.isin(df_re_donation.npe03__contact__c)]
In [580]:
# Vamos a analizar la tabla recurring donation
df=df_contactos_f
In [581]:
# Se crea una lista por ahora vacia, en la que se irán añadiendo las variables que se van a eliminar del dataset por motivos varios: no utilidad, gran volumen de nulos, ...
col_to_delete_contactos=list()
Analsis de distribución por variables
-> msf_seniority__c: Variable numerica
In [582]:
# Vamos a realizar analisis por cada variable
var = "msf_seniority__c"
In [583]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_seniority__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_seniority__c es 0. Lo que supone un 0.0%
In [584]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[584]:
# Tot % Tot
0.0 72843 7.327798
8.0 69373 6.978726
7.0 68120 6.852677
6.0 65874 6.626736
9.0 64102 6.448478
5.0 47211 4.749292
10.0 43910 4.417221
12.0 43340 4.359880
4.0 39932 4.017045
13.0 38984 3.921679
11.0 37505 3.772896
14.0 36133 3.634877
18.0 34795 3.500278
2.0 31380 3.156738
17.0 31370 3.155732
1.0 30543 3.072539
16.0 30132 3.031193
15.0 28826 2.899813
3.0 27544 2.770848
19.0 26257 2.641379
29.0 19570 1.968686
20.0 18961 1.907422
23.0 13852 1.393472
22.0 10824 1.088863
21.0 9968 1.002752
24.0 9882 0.994101
28.0 9759 0.981728
25.0 9327 0.938270
27.0 6960 0.700156
31.0 6128 0.616459
26.0 4656 0.468380
30.0 4424 0.445042
32.0 881 0.088626
35.0 216 0.021729
34.0 215 0.021628
33.0 171 0.017202
36.0 84 0.008450
37.0 12 0.001207
msf_seniority__c: Número de años desde la fecha de su primera aportación económica hasta día de hoy.
Se puede observar que está bastante distribuido, siendo en 0 donde se acumula la mayor parte de la población. Se analizará posteriormente si la categorización de la variable en grupos pueda dar buenos resultados.
Analsis de distribución por variables
-> npo02__best_gift_year__c: Variable numerica
In [585]:
# Vamos a realizar analisis por cada variable
var = "npo02__best_gift_year__c"
In [586]:
# Analizamos nulos
count_nulos(df_contactos,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__best_gift_year__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__best_gift_year__c es 709207. Lo que supone un 39.32569192184401%
Out[586]:
['npo02__best_gift_year__c']
In [587]:
# Analizamos posibles valores de la variable
freq_variables(df_contactos,var)
Out[587]:
# Tot % Tot
709207 39.325692
2018 303667 16.838405
2022 185032 10.260067
2021 93074 5.160975
2020 90828 5.036434
2019 77054 4.272662
2023 55899 3.099612
2010 29210 1.619701
1994 28224 1.565027
2017 21245 1.178040
2005 15932 0.883433
2014 14681 0.814065
2011 14643 0.811958
2004 13160 0.729725
2000 12659 0.701944
2015 11996 0.665181
2001 11403 0.632299
1998 11363 0.630081
2013 10940 0.606626
2016 9948 0.551619
2003 9537 0.528829
2008 8465 0.469386
1999 8142 0.451476
2009 7599 0.421366
1996 6869 0.380888
2012 6795 0.376784
2006 6723 0.372792
1992 6238 0.345899
2007 5562 0.308414
2002 4753 0.263555
1997 4491 0.249027
1995 4064 0.225350
1993 2470 0.136962
1991 624 0.034601
1989 435 0.024121
1990 212 0.011755
1988 187 0.010369
1987 88 0.004880
npo02__best_gift_year__c: Año fiscal en que se ha realizado mayor importe total.
Se puede observar que hay casi un 40% de los registros a vacio. Se analizará posteriormente por si son clientes sin ninguna aportacion y poder analizar si es vacio con sentido o no.
Analsis de distribución por variables
-> msf_birthyear__c: Variable numerica
In [588]:
# Vamos a realizar analisis por cada variable
var = "msf_birthyear__c"
In [589]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_birthyear__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_birthyear__c es 216719. Lo que supone un 21.8013125915434%
Out[589]:
['npo02__best_gift_year__c', 'msf_birthyear__c']
In [590]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[590]:
# Tot % Tot
216719 21.801313
1964 17594 1.769906
1963 17337 1.744053
1965 17330 1.743349
1968 17142 1.724436
1959 17014 1.711560
1966 16945 1.704619
1958 16925 1.702607
1962 16912 1.701299
1973 16892 1.699287
1975 16872 1.697275
1974 16840 1.694056
1961 16738 1.683795
1960 16698 1.679771
1972 16694 1.679369
1957 16680 1.677960
1967 16658 1.675747
1976 16577 1.667599
1969 16530 1.662871
1971 16525 1.662368
1970 16374 1.647178
1977 16072 1.616797
1978 15831 1.592553
1956 15179 1.526964
1979 15167 1.525757
1980 14467 1.455339
1955 14076 1.416005
1981 13669 1.375062
1954 13015 1.309272
1982 12607 1.268228
1953 12371 1.244487
1983 11938 1.200929
1952 11928 1.199923
1984 11031 1.109687
1951 10886 1.095101
1950 10448 1.051039
1985 10209 1.026996
1949 10096 1.015629
1948 9717 0.977502
1986 9190 0.924488
1987 8599 0.865035
1947 8534 0.858496
1988 7972 0.801960
1946 7749 0.779527
1945 7715 0.776107
1989 7665 0.771077
1990 7188 0.723092
1991 7174 0.721684
1992 7100 0.714240
1996 6956 0.699754
1994 6952 0.699351
1995 6929 0.697038
1993 6812 0.685268
1997 6806 0.684664
1943 6730 0.677019
1944 6682 0.672190
1999 6496 0.653479
1998 6406 0.644425
2000 6137 0.617365
2001 5321 0.535277
1942 5128 0.515862
1940 4817 0.484576
1941 4598 0.462546
2002 4409 0.443533
2003 3408 0.342835
1936 3275 0.329456
1938 3130 0.314869
1939 3080 0.309839
1937 3037 0.305514
1935 2925 0.294247
1934 2560 0.257529
1933 2251 0.226444
1932 2104 0.211656
2004 1909 0.192040
1930 1864 0.187513
1931 1800 0.181075
1929 1306 0.131380
1928 1202 0.120918
1927 966 0.097177
1926 803 0.080780
1925 717 0.072128
1924 623 0.062672
1923 478 0.048085
1922 453 0.045571
1921 336 0.033801
2020 276 0.027765
1920 258 0.025954
1919 235 0.023640
2005 216 0.021729
2006 157 0.015794
1918 150 0.015090
2017 129 0.012977
1917 123 0.012373
2008 117 0.011770
2016 113 0.011367
2007 110 0.011066
2019 107 0.010764
1916 98 0.009859
2014 88 0.008853
2015 84 0.008450
2013 81 0.008148
2018 73 0.007344
2021 69 0.006941
1915 68 0.006841
2010 67 0.006740
2012 66 0.006639
2009 65 0.006539
1914 59 0.005935
2011 56 0.005633
1913 40 0.004024
1911 29 0.002917
1912 20 0.002012
2022 18 0.001811
1910 17 0.001710
1909 14 0.001408
2023 13 0.001308
1907 9 0.000905
1904 8 0.000805
1906 8 0.000805
1908 8 0.000805
1902 5 0.000503
1903 4 0.000402
1901 3 0.000302
1900 3 0.000302
1905 2 0.000201
1712 1 0.000101
1893 1 0.000101
1897 1 0.000101
msf_birthyear__c: .
Se puede observar que hay más de un 20% de los registros a vacio.
In [ ]:
 
Analsis de distribución por variables
-> msf_entrycampaign__c: Variable string
In [591]:
# Vamos a realizar analisis por cada variable
var = "msf_entrycampaign__c"
In [592]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_entrycampaign__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_entrycampaign__c es 74. Lo que supone un 0.007444188704147821%
In [593]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[593]:
# Tot % Tot
7013Y000001mr4CQAQ 37526 3.775008
7013Y000001mrCzQAI 36910 3.713041
7013Y000001mr2DQAQ 30523 3.070527
7013Y000001mr2cQAA 25776 2.592992
7013Y000001mrBSQAY 24519 2.466541
... ... ...
7013Y000001mrJ5QAI 1 0.000101
7013Y000001gt85QAA 1 0.000101
7013Y000001n7z7QAA 1 0.000101
7013Y000001mrEWQAY 1 0.000101
7013Y000001mre3QAA 1 0.000101

2987 rows × 2 columns

msf_birthyear__c: .
Se puede observar que hay más de un 20% de los registros a vacio.
Analsis de distribución por variables
-> LeadSource: Variable categorica
In [594]:
# Vamos a realizar analisis por cada variable
var = "leadsource"
In [595]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable leadsource es 0. Lo que supone un 0.0%
El nº de vacios para la variable leadsource es 20. Lo que supone un 0.0020119428930129245%
In [596]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[596]:
# Tot % Tot
Persona a persona 346349 34.841720
Otro 229312 23.068132
Telemarketing 153973 15.489244
Personal con tablet 70560 7.098135
Cupón 65399 6.578953
Web MSF 64678 6.506422
Teléfono campaña 34878 3.508627
Web terceros 14767 1.485518
Web campaña 4795 0.482363
Teléfono web 4304 0.432970
Eventos 1969 0.198076
Teléfono SAS 1524 0.153310
Email a SAS 921 0.092650
Email a Empresas 138 0.013882
Plataforma iniciativas 127 0.012776
Email a Bodas 124 0.012474
Correo postal sin cupón 92 0.009255
Entidad financiera 87 0.008752
Teléfono Officers 23 0.002314
20 0.002012
Teléfono Herencias y Legados 4 0.000402
Email a Iniciativas Solidarias 3 0.000302
Cloud page 3 0.000302
Email herencias 3 0.000302
Email a One to one 3 0.000302
Email a officers Mid Donors 2 0.000201
Tel?fono SAS 2 0.000201
Email Director/a General 1 0.000101
SMS 1 0.000101
TelEfono officers 1 0.000101
Redes Sociales 1 0.000101
leadsource: Canal principal.
Se puede observar que casi no hay vacios. La mayor parte es Persona a Persona.
Analsis de distribución por variables
-> msf_firstcampaigncolaborationchannel__c: Variable categorica
In [597]:
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaigncolaborationchannel__c"
In [598]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstcampaigncolaborationchannel__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_firstcampaigncolaborationchannel__c es 139277. Lo que supone un 14.010868515508058%
In [599]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[599]:
# Tot % Tot
Persona a persona 310662 31.251710
Telemarketing 162367 16.333657
139277 14.010869
Otro 113431 11.410835
Cupón 67920 6.832558
Web MSF 67860 6.826522
Personal con tablet 65618 6.600983
Teléfono campaña 36839 3.705898
Web terceros 12878 1.295490
Teléfono web 4501 0.452788
Web campaña 4339 0.436491
Teléfono SAS 2498 0.251292
Email a SAS 1437 0.144558
Eventos 1435 0.144357
Plataforma iniciativas 1006 0.101201
web campaña 624 0.062773
Entidad financiera 583 0.058648
cupón 224 0.022534
Web MSF Mi perfil 141 0.014184
Email a Empresas 123 0.012373
Correo postal sin cupón 119 0.011971
Email a Bodas 103 0.010362
Teléfono Officers 63 0.006338
Email a officers Mid Donors 4 0.000402
Email a Iniciativas Solidarias 4 0.000402
Email a One to one 3 0.000302
Cloud page 2 0.000201
Email Director/a General 1 0.000101
Email a one to one 1 0.000101
Email herencias 1 0.000101
msf_firstcampaigncolaborationchannel__c: Canal por el que realizó la primera donación.
Se puede observar que hay un 15% de vacios. Como estamos analizando la poblacion de donaciones recurrentes, el que no haya realizado ninguna donación, no tiene sentido, por lo que son vacios. La información se parece a la del campo LeadSource, y tiene más vacios, por lo que nos quedaremos con la anterior para el modelo.
In [600]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("msf_firstcampaigncolaborationchannel__c")
col_to_delete_contactos
Out[600]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c']
Analsis de distribución por variables
-> npo02__AverageAmount__c: Variable numerica
In [601]:
# Vamos a realizar analisis por cada variable
var = "npo02__averageamount__c"
In [602]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__averageamount__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__averageamount__c es 0. Lo que supone un 0.0%
In [603]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[603]:
# Tot % Tot
0.0 994064 100.0
npo02__averageamount__c: Media del total de aportaciones.
Se puede observar que no hay vacios pero está informado a 0 para todos los casos.
In [604]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("npo02__averageamount__c")
col_to_delete_contactos
Out[604]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c']
Analsis de distribución por variables
-msf_isactivedonor__c: Variable categorica
In [605]:
# Vamos a realizar analisis por cada variable
var = "msf_isactivedonor__c"
In [606]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_isactivedonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_isactivedonor__c es 0. Lo que supone un 0.0%
In [607]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[607]:
# Tot % Tot
Nunca 727574 73.191867
Exdonante 212374 21.364218
Donante 54116 5.443915
msf_isactivedonor__c: XXXXXXXXX.
Se puede observar que no hay vacios, aunque que el campo se informe como nunca en la mayor parte de los casos, para los clientes comunes con la tabla de recurring donation no tiene mucho sentido, habrá que analizar en conjunto con otras variables de esa tabla.
In [608]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("msf_isactivedonor__c")
col_to_delete_contactos
Out[608]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c']
Analsis de distribución por variables
-> msf_isactiverecurringdonor__c: Variable categorica
In [609]:
# Vamos a realizar analisis por cada variable
var = "msf_isactiverecurringdonor__c"
In [610]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_isactiverecurringdonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_isactiverecurringdonor__c es 0. Lo que supone un 0.0%
In [611]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[611]:
# Tot % Tot
Baja 511080 51.413189
Socio 482224 48.510357
Nunca 760 0.076454
msf_isactiverecurringdonor__c: indicador de socio recurrente.
Se puede observar que no hay vacios, pero no añade más información que el campo de activo de la tabla de recurring donation. Por lo que esta variable y la anterior se eliminarán de esta tabla.
In [612]:
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("msf_isactiverecurringdonor__c")
col_to_delete_contactos
Out[612]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c']
Analsis de distribución por variables
-> npsp__deceased__c: Variable categorica
In [613]:
# Vamos a realizar analisis por cada variable
var = "npsp__deceased__c"
In [614]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__deceased__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npsp__deceased__c es 0. Lo que supone un 0.0%
In [615]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[615]:
# Tot % Tot
False 971783 97.758595
True 22281 2.241405
npsp__deceased__c: Indicador de fallecido
Se puede observar que no hay vacios, solo el 2% han fallecido. Esto quiere decir que se podrán usar en el modelo para prdecir, pero no para aplicar.
Analsis de distribución por variables
-> msf_begindatemsf__c: Variable categorica
In [616]:
# Vamos a realizar analisis por cada variable
var = "msf_begindatemsf__c"
In [617]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_begindatemsf__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_begindatemsf__c es 0. Lo que supone un 0.0%
In [618]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[618]:
# Tot % Tot
2004-01-01 4833 0.486186
2000-02-01 4462 0.448864
2000-01-01 3903 0.392631
1994-10-01 3486 0.350682
1995-02-01 3386 0.340622
... ... ...
1996-05-13 1 0.000101
1996-03-28 1 0.000101
1996-10-27 1 0.000101
1996-07-31 1 0.000101
1994-09-18 1 0.000101

10115 rows × 2 columns

msf_begindatemsf__c: Fecha de entrada en MSF.
Se puede observar que no hay vacios, se podrá tranformar en "tiempo en MSF" teniendo en cuenta la fecha de baja y "tiempo desde entrada hasta donación" quizá pueda ser util en el modelo.
Analsis de distribución por variables
-> msf_fechacambiolevelrelacion__c: Variable categorica
In [619]:
# Vamos a realizar analisis por cada variable
var = "msf_fechacambiolevelrelacion__c"
In [620]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_fechacambiolevelrelacion__c es 4. Lo que supone un 0.00040238857860258495%
El nº de vacios para la variable msf_fechacambiolevelrelacion__c es 0. Lo que supone un 0.0%
In [621]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[621]:
# Tot % Tot
2020-03-28 852024 85.711526
2020-07-20 6964 0.700561
2020-09-22 4511 0.453796
2020-09-19 3664 0.368589
2022-01-02 2462 0.247671
2020-09-21 1639 0.164879
2023-01-03 1481 0.148985
2020-09-20 1466 0.147476
2021-01-04 1023 0.102911
2023-01-02 777 0.078164
2022-01-15 707 0.071122
2022-05-06 536 0.053920
2022-03-22 529 0.053216
2020-12-04 522 0.052512
2022-06-04 491 0.049393
2021-06-18 472 0.047482
2022-12-03 461 0.046375
2022-05-11 445 0.044766
2021-03-04 439 0.044162
2022-12-23 434 0.043659
2020-10-03 431 0.043358
2020-09-23 425 0.042754
2022-10-21 418 0.042050
2023-02-10 403 0.040541
2021-07-08 398 0.040038
2021-02-05 395 0.039736
2022-09-23 387 0.038931
2021-08-07 380 0.038227
2022-02-05 379 0.038126
2022-11-24 376 0.037825
2021-06-25 374 0.037623
2021-11-18 373 0.037523
2022-03-11 371 0.037322
2020-09-25 371 0.037322
2021-03-11 368 0.037020
2021-01-03 366 0.036819
2021-05-13 365 0.036718
2023-02-23 361 0.036316
2023-02-09 356 0.035813
2020-11-20 349 0.035109
2020-11-27 349 0.035109
2022-03-05 347 0.034907
2022-05-19 334 0.033600
2022-03-12 333 0.033499
2023-01-26 332 0.033398
2022-03-09 329 0.033097
2020-11-06 325 0.032694
2021-05-08 322 0.032392
2023-03-30 322 0.032392
2022-09-08 318 0.031990
2021-12-03 312 0.031386
2023-02-21 311 0.031286
2022-11-18 309 0.031085
2021-01-28 308 0.030984
2021-03-18 308 0.030984
2023-02-17 308 0.030984
2022-07-07 307 0.030883
2022-03-10 306 0.030783
2021-03-06 304 0.030582
2021-11-07 303 0.030481
2021-11-13 301 0.030280
2022-06-17 298 0.029978
2021-05-27 298 0.029978
2021-02-11 295 0.029676
2021-05-20 294 0.029576
2022-12-20 294 0.029576
2021-01-21 288 0.028972
2023-02-15 285 0.028670
2020-09-24 285 0.028670
2022-10-06 278 0.027966
2021-04-17 277 0.027866
2022-05-28 276 0.027765
2021-04-29 275 0.027664
2021-07-03 274 0.027564
2022-03-17 273 0.027463
2023-04-05 272 0.027363
2022-03-04 271 0.027262
2021-04-22 270 0.027161
2020-10-24 268 0.026960
2021-06-09 268 0.026960
2020-09-29 266 0.026759
2021-05-29 264 0.026558
2022-12-15 264 0.026558
2021-02-18 264 0.026558
2021-03-25 264 0.026558
2023-05-26 260 0.026155
2021-12-17 260 0.026155
2022-12-04 260 0.026155
2022-11-10 257 0.025854
2021-09-04 256 0.025753
2021-11-30 255 0.025652
2023-02-16 255 0.025652
2021-06-29 252 0.025351
2020-10-29 251 0.025250
2022-03-15 251 0.025250
2020-10-22 250 0.025149
2021-06-05 250 0.025149
2022-11-17 248 0.024948
2022-04-12 246 0.024747
2022-03-24 245 0.024646
2021-06-03 244 0.024546
2022-09-28 244 0.024546
2023-02-08 243 0.024445
2023-03-10 243 0.024445
2020-11-17 243 0.024445
2022-01-28 242 0.024345
2022-02-03 242 0.024345
2022-11-30 242 0.024345
2022-03-16 241 0.024244
2021-12-22 239 0.024043
2022-09-30 237 0.023842
2021-04-01 237 0.023842
2023-02-12 237 0.023842
2023-07-05 236 0.023741
2022-11-11 234 0.023540
2023-03-16 233 0.023439
2022-05-12 232 0.023339
2022-03-08 232 0.023339
2021-02-25 231 0.023238
2022-03-18 231 0.023238
2022-08-06 231 0.023238
2022-11-25 231 0.023238
2023-02-24 231 0.023238
2022-12-16 230 0.023137
2023-03-23 230 0.023137
2020-10-06 229 0.023037
2021-09-09 229 0.023037
2022-11-26 225 0.022634
2021-02-10 225 0.022634
2021-10-29 224 0.022534
2022-11-16 223 0.022433
2023-05-11 223 0.022433
2021-02-17 222 0.022333
2023-06-16 221 0.022232
2022-03-31 221 0.022232
2022-12-10 220 0.022131
2020-12-25 219 0.022031
2021-04-16 219 0.022031
2021-11-25 219 0.022031
2021-07-29 218 0.021930
2021-07-22 217 0.021830
2022-03-25 216 0.021729
2023-05-12 216 0.021729
2023-03-17 216 0.021729
2023-03-31 214 0.021528
2023-04-14 214 0.021528
2022-07-22 214 0.021528
2022-10-20 213 0.021427
2022-11-23 213 0.021427
2023-01-19 213 0.021427
2022-04-09 213 0.021427
2020-12-30 212 0.021327
2023-04-21 212 0.021327
2023-04-07 212 0.021327
2021-07-01 212 0.021327
2023-02-18 210 0.021125
2021-10-03 210 0.021125
2022-10-14 210 0.021125
2021-10-01 210 0.021125
2021-02-24 209 0.021025
2022-11-09 209 0.021025
2022-11-01 209 0.021025
2022-04-28 208 0.020924
2021-06-10 207 0.020824
2022-10-04 206 0.020723
2022-12-17 206 0.020723
2023-05-25 206 0.020723
2022-10-27 205 0.020622
2022-11-08 205 0.020622
2022-12-21 205 0.020622
2023-02-11 205 0.020622
2023-02-01 204 0.020522
2021-09-22 203 0.020421
2021-11-19 202 0.020321
2021-01-14 201 0.020220
2020-12-19 201 0.020220
2021-03-27 201 0.020220
2022-05-13 200 0.020120
2022-12-01 200 0.020120
2022-11-22 200 0.020120
2021-12-23 200 0.020120
2023-06-22 200 0.020120
2021-11-11 199 0.020019
2023-04-19 198 0.019918
2022-01-06 198 0.019918
2022-10-28 198 0.019918
2023-01-27 197 0.019818
2022-02-24 197 0.019818
2021-10-16 197 0.019818
2021-10-23 196 0.019717
2021-10-22 196 0.019717
2022-04-08 196 0.019717
2023-02-28 195 0.019617
2023-06-29 195 0.019617
2023-04-28 195 0.019617
2021-05-06 194 0.019516
2022-11-19 194 0.019516
2023-06-01 194 0.019516
2021-12-14 193 0.019415
2022-12-14 193 0.019415
2021-07-15 193 0.019415
2023-06-09 192 0.019315
2023-06-30 191 0.019214
2022-06-15 191 0.019214
2022-06-23 191 0.019214
2023-06-15 191 0.019214
2020-10-15 191 0.019214
2022-05-20 190 0.019114
2023-05-24 189 0.019013
2021-09-23 189 0.019013
2022-05-10 189 0.019013
2020-12-15 187 0.018812
2022-02-11 187 0.018812
2022-02-18 186 0.018711
2023-03-24 186 0.018711
2023-01-21 186 0.018711
2021-04-08 186 0.018711
2023-06-21 186 0.018711
2022-10-07 185 0.018611
2021-01-06 185 0.018611
2023-04-26 185 0.018611
2022-11-15 185 0.018611
2022-04-29 185 0.018611
2023-05-09 185 0.018611
2023-06-28 184 0.018510
2023-04-27 184 0.018510
2023-01-31 183 0.018409
2021-06-04 183 0.018409
2020-12-11 182 0.018309
2022-06-29 182 0.018309
2021-05-16 181 0.018208
2022-04-01 181 0.018208
2021-06-01 180 0.018108
2023-07-07 180 0.018108
2021-09-24 180 0.018108
2022-01-27 179 0.018007
2020-10-30 179 0.018007
2020-12-17 179 0.018007
2023-06-03 179 0.018007
2023-02-25 179 0.018007
2021-09-16 179 0.018007
2023-06-06 178 0.017906
2022-11-29 178 0.017906
2021-12-16 178 0.017906
2023-01-20 178 0.017906
2022-07-15 178 0.017906
2023-02-07 177 0.017806
2023-06-17 177 0.017806
2021-10-15 177 0.017806
2022-06-10 177 0.017806
2023-07-06 177 0.017806
2023-02-14 176 0.017705
2022-03-23 176 0.017705
2023-01-14 176 0.017705
2023-06-23 176 0.017705
2022-04-14 176 0.017705
2021-09-30 176 0.017705
2022-10-25 175 0.017605
2023-02-05 175 0.017605
2022-09-04 175 0.017605
2021-04-15 175 0.017605
2022-05-27 175 0.017605
2022-03-29 175 0.017605
2021-10-19 175 0.017605
2023-05-31 175 0.017605
2022-07-14 174 0.017504
2021-02-03 174 0.017504
2020-12-22 174 0.017504
2023-06-08 174 0.017504
2022-06-09 174 0.017504
2021-05-21 173 0.017403
2021-10-26 173 0.017403
2022-05-14 173 0.017403
2022-07-21 173 0.017403
2020-10-20 172 0.017303
2023-01-06 172 0.017303
2022-06-08 172 0.017303
2022-10-12 171 0.017202
2021-02-23 171 0.017202
2021-04-23 171 0.017202
2020-11-13 171 0.017202
2020-10-10 171 0.017202
2021-02-06 170 0.017102
2023-01-12 170 0.017102
2021-11-16 170 0.017102
2023-05-18 170 0.017102
2023-03-09 170 0.017102
2023-05-27 170 0.017102
2022-02-26 170 0.017102
2021-12-01 170 0.017102
2023-01-28 169 0.017001
2022-10-15 168 0.016900
2021-02-26 168 0.016900
2020-10-14 168 0.016900
2022-06-24 168 0.016900
2022-10-26 167 0.016800
2021-12-19 167 0.016800
2022-11-12 166 0.016699
2021-12-21 166 0.016699
2023-05-07 166 0.016699
2023-01-04 166 0.016699
2023-02-03 165 0.016599
2021-12-15 165 0.016599
2021-11-27 165 0.016599
2022-07-13 164 0.016498
2022-07-10 164 0.016498
2023-01-13 164 0.016498
2020-12-03 164 0.016498
2022-07-28 164 0.016498
2023-05-19 163 0.016397
2021-10-21 163 0.016397
2022-03-01 163 0.016397
2022-07-01 163 0.016397
2022-02-17 162 0.016297
2021-02-16 161 0.016196
2020-12-14 161 0.016196
2021-05-28 161 0.016196
2023-03-01 161 0.016196
2022-05-17 161 0.016196
2021-10-09 160 0.016096
2021-10-07 160 0.016096
2023-03-08 160 0.016096
2022-09-15 160 0.016096
2023-06-24 160 0.016096
2023-04-22 159 0.015995
2021-06-19 159 0.015995
2023-01-25 159 0.015995
2022-04-22 159 0.015995
2022-10-18 159 0.015995
2021-02-04 159 0.015995
2022-01-19 158 0.015894
2021-01-20 158 0.015894
2022-06-01 158 0.015894
2021-09-15 158 0.015894
2023-05-13 157 0.015794
2022-02-16 157 0.015794
2020-10-16 157 0.015794
2022-07-08 156 0.015693
2021-11-24 156 0.015693
2023-03-26 156 0.015693
2022-10-01 156 0.015693
2022-09-17 156 0.015693
2021-09-17 155 0.015593
2022-01-18 155 0.015593
2021-04-27 155 0.015593
2021-04-21 155 0.015593
2023-04-25 155 0.015593
2021-06-12 155 0.015593
2021-01-26 155 0.015593
2021-12-08 155 0.015593
2022-11-06 154 0.015492
2022-01-21 154 0.015492
2021-07-09 154 0.015492
2022-02-23 154 0.015492
2022-10-09 154 0.015492
2022-12-06 153 0.015391
2021-05-15 152 0.015291
2022-06-30 152 0.015291
2020-11-28 152 0.015291
2022-01-20 152 0.015291
2021-10-08 152 0.015291
2023-01-17 152 0.015291
2021-07-23 151 0.015190
2022-02-10 151 0.015190
2022-10-22 151 0.015190
2023-03-14 150 0.015090
2023-05-23 150 0.015090
2023-01-24 150 0.015090
2021-09-29 150 0.015090
2022-05-03 150 0.015090
2021-09-10 150 0.015090
2021-03-30 149 0.014989
2023-03-18 149 0.014989
2022-10-29 148 0.014888
2022-06-11 148 0.014888
2022-03-26 148 0.014888
2022-03-30 148 0.014888
2021-05-22 148 0.014888
2021-04-09 148 0.014888
2020-10-31 148 0.014888
2021-07-16 148 0.014888
2022-03-19 148 0.014888
2022-10-19 147 0.014788
2021-04-28 147 0.014788
2020-10-01 147 0.014788
2022-06-22 147 0.014788
2021-05-01 147 0.014788
2021-11-20 147 0.014788
2022-10-11 147 0.014788
2022-09-29 146 0.014687
2022-05-31 146 0.014687
2023-07-08 146 0.014687
2021-03-19 146 0.014687
2022-06-16 146 0.014687
2021-03-12 146 0.014687
2021-04-20 145 0.014587
2022-07-16 145 0.014587
2022-01-13 145 0.014587
2021-07-30 144 0.014486
2022-01-10 144 0.014486
2022-12-30 144 0.014486
2022-05-08 144 0.014486
2023-05-30 144 0.014486
2021-06-11 144 0.014486
2021-04-30 144 0.014486
2022-03-07 144 0.014486
2022-09-16 143 0.014385
2023-03-03 143 0.014385
2023-06-14 143 0.014385
2021-01-23 143 0.014385
2021-06-24 143 0.014385
2022-12-29 142 0.014285
2020-11-10 142 0.014285
2023-04-20 142 0.014285
2023-03-05 142 0.014285
2022-05-21 142 0.014285
2021-07-31 141 0.014184
2021-10-30 141 0.014184
2021-11-23 141 0.014184
2023-03-21 141 0.014184
2020-11-14 141 0.014184
2021-10-28 141 0.014184
2021-09-18 140 0.014084
2022-05-24 140 0.014084
2023-03-15 140 0.014084
2021-11-10 140 0.014084
2022-03-03 140 0.014084
2021-12-10 140 0.014084
2020-11-19 140 0.014084
2021-12-05 140 0.014084
2022-08-11 139 0.013983
2020-12-08 139 0.013983
2021-05-26 138 0.013882
2022-02-22 138 0.013882
2022-02-08 138 0.013882
2021-06-30 138 0.013882
2021-05-11 138 0.013882
2020-11-21 138 0.013882
2021-05-18 137 0.013782
2022-07-20 137 0.013782
2021-02-20 137 0.013782
2021-12-24 137 0.013782
2021-01-16 137 0.013782
2021-11-06 137 0.013782
2021-09-28 137 0.013782
2021-12-11 137 0.013782
2021-05-05 136 0.013681
2023-06-20 136 0.013681
2023-03-07 136 0.013681
2022-07-23 136 0.013681
2022-02-12 136 0.013681
2022-01-12 136 0.013681
2022-09-27 136 0.013681
2020-12-16 136 0.013681
2021-01-29 136 0.013681
2020-12-01 135 0.013581
2020-10-27 135 0.013581
2023-03-11 135 0.013581
2020-12-18 135 0.013581
2020-12-12 135 0.013581
2023-04-01 135 0.013581
2022-02-25 135 0.013581
2023-03-22 135 0.013581
2021-10-20 135 0.013581
2021-03-26 135 0.013581
2022-04-13 135 0.013581
2020-12-29 135 0.013581
2022-09-24 134 0.013480
2021-01-09 134 0.013480
2022-04-26 134 0.013480
2022-04-27 134 0.013480
2021-07-10 134 0.013480
2023-05-16 134 0.013480
2021-03-24 133 0.013379
2023-05-17 133 0.013379
2021-10-04 133 0.013379
2021-03-13 133 0.013379
2021-10-27 133 0.013379
2021-02-27 133 0.013379
2022-08-04 133 0.013379
2023-01-18 132 0.013279
2021-10-12 132 0.013279
2021-09-08 132 0.013279
2021-12-25 132 0.013279
2021-07-14 131 0.013178
2021-02-13 131 0.013178
2021-04-14 130 0.013078
2021-09-25 130 0.013078
2021-08-04 130 0.013078
2021-06-23 130 0.013078
2020-12-24 130 0.013078
2022-11-03 130 0.013078
2022-02-01 129 0.012977
2022-09-10 129 0.012977
2021-03-17 129 0.012977
2020-10-17 128 0.012876
2020-11-04 128 0.012876
2022-12-24 128 0.012876
2022-05-26 127 0.012776
2021-06-16 127 0.012776
2022-12-28 127 0.012776
2020-11-24 127 0.012776
2021-10-05 127 0.012776
2020-11-26 127 0.012776
2021-12-29 127 0.012776
2022-04-15 126 0.012675
2023-06-10 126 0.012675
2021-05-25 126 0.012675
2020-11-25 126 0.012675
2022-06-14 126 0.012675
2021-10-06 125 0.012575
2022-07-29 125 0.012575
2022-01-29 125 0.012575
2023-06-27 125 0.012575
2022-09-09 124 0.012474
2021-05-04 124 0.012474
2021-06-15 124 0.012474
2023-03-28 124 0.012474
2021-01-19 124 0.012474
2023-01-11 123 0.012373
2022-09-22 123 0.012373
2021-07-28 123 0.012373
2023-06-13 123 0.012373
2023-04-13 123 0.012373
2022-04-03 123 0.012373
2022-09-14 122 0.012273
2021-04-24 122 0.012273
2022-06-21 122 0.012273
2020-10-28 122 0.012273
2023-04-29 122 0.012273
2022-12-08 121 0.012172
2021-03-31 121 0.012172
2021-05-19 121 0.012172
2022-08-05 121 0.012172
2021-12-30 121 0.012172
2021-02-09 121 0.012172
2022-07-30 121 0.012172
2022-04-23 120 0.012072
2021-09-11 120 0.012072
2023-05-04 120 0.012072
2022-07-12 120 0.012072
2021-11-09 119 0.011971
2022-02-19 119 0.011971
2023-05-20 119 0.011971
2020-12-23 119 0.011971
2023-07-01 119 0.011971
2022-01-26 118 0.011871
2023-05-06 117 0.011770
2023-01-10 117 0.011770
2021-01-15 117 0.011770
2022-04-07 117 0.011770
2020-11-12 116 0.011669
2021-04-13 116 0.011669
2021-02-12 115 0.011569
2022-01-22 115 0.011569
2021-01-31 114 0.011468
2022-01-08 114 0.011468
2022-04-30 114 0.011468
2021-03-16 114 0.011468
2020-10-08 113 0.011368
2020-10-23 113 0.011368
2022-01-25 113 0.011368
2023-05-10 113 0.011368
2021-07-21 113 0.011368
2021-11-26 113 0.011368
2023-04-15 113 0.011368
2021-07-27 112 0.011267
2020-11-18 112 0.011267
2021-03-09 112 0.011267
2023-05-03 112 0.011267
2022-02-15 112 0.011267
2022-12-13 111 0.011166
2022-02-09 111 0.011166
2021-02-19 111 0.011166
2022-04-21 111 0.011166
2021-09-03 111 0.011166
2022-11-05 110 0.011066
2022-06-18 110 0.011066
2021-06-26 110 0.011066
2021-01-22 110 0.011066
2022-07-03 110 0.011066
2023-01-05 109 0.010965
2022-07-19 109 0.010965
2020-11-11 109 0.010965
2022-12-31 109 0.010965
2023-04-18 108 0.010865
2023-04-12 108 0.010865
2022-08-10 106 0.010663
2021-07-13 106 0.010663
2021-08-11 106 0.010663
2021-09-07 105 0.010563
2021-07-24 105 0.010563
2022-06-07 105 0.010563
2022-06-03 105 0.010563
2021-01-08 104 0.010462
2021-01-27 104 0.010462
2021-03-01 103 0.010362
2021-08-10 102 0.010261
2020-12-31 102 0.010261
2020-10-21 101 0.010160
2022-07-27 101 0.010160
2021-10-14 100 0.010060
2021-07-17 100 0.010060
2022-08-12 99 0.009959
2022-06-25 97 0.009758
2023-06-07 97 0.009758
2022-09-20 97 0.009758
2021-12-31 97 0.009758
2021-07-20 96 0.009657
2021-03-20 96 0.009657
2022-04-20 95 0.009557
2021-04-10 94 0.009456
2021-07-07 94 0.009456
2022-07-26 93 0.009356
2020-12-05 93 0.009356
2021-01-01 93 0.009356
2021-12-28 92 0.009255
2022-08-03 92 0.009255
2020-10-09 90 0.009054
2021-04-07 90 0.009054
2021-11-03 89 0.008953
2021-03-10 89 0.008953
2022-01-11 88 0.008853
2023-03-04 88 0.008853
2022-05-04 87 0.008752
2021-09-14 86 0.008651
2020-07-16 85 0.008551
2020-11-05 80 0.008048
2021-03-23 78 0.007847
2022-09-13 76 0.007645
2021-01-12 76 0.007645
2022-08-13 75 0.007545
2022-10-03 75 0.007545
2022-06-28 75 0.007545
2022-04-05 74 0.007444
2021-08-18 74 0.007444
2022-01-14 73 0.007344
2020-12-06 73 0.007344
2022-08-17 72 0.007243
2021-04-03 72 0.007243
2020-10-04 71 0.007142
2020-12-21 70 0.007042
2022-08-09 69 0.006941
2021-08-06 69 0.006941
2021-08-01 68 0.006841
2020-10-25 67 0.006740
2021-12-04 67 0.006740
2022-01-01 66 0.006639
2023-04-11 66 0.006639
2022-03-20 66 0.006639
2022-03-14 66 0.006639
2020-12-10 64 0.006438
2021-01-13 63 0.006338
2022-03-13 60 0.006036
2022-12-27 59 0.005935
2021-08-13 59 0.005935
2021-09-01 58 0.005835
2021-09-21 57 0.005734
2021-08-19 56 0.005633
2022-04-19 55 0.005533
2020-09-27 55 0.005533
2020-09-15 54 0.005432
2021-11-05 52 0.005231
2023-06-04 50 0.005030
2022-08-31 49 0.004929
2021-04-06 47 0.004728
2021-08-26 47 0.004728
2022-01-30 46 0.004627
2022-05-25 46 0.004627
2022-08-24 46 0.004627
2021-01-07 46 0.004627
2020-11-08 45 0.004527
2021-08-31 44 0.004426
2021-03-05 42 0.004225
2021-08-25 42 0.004225
2021-08-12 41 0.004124
2021-08-17 40 0.004024
2023-02-13 40 0.004024
2022-07-05 39 0.003923
2021-08-28 37 0.003722
2021-08-24 35 0.003521
2021-08-14 35 0.003521
2021-08-20 33 0.003320
2020-09-26 32 0.003219
2022-11-04 32 0.003219
2021-03-03 32 0.003219
2021-11-04 29 0.002917
2022-02-28 29 0.002917
2023-03-29 28 0.002817
2021-01-05 27 0.002716
2020-10-05 25 0.002515
2022-09-21 25 0.002515
2023-06-25 24 0.002414
2021-08-27 24 0.002414
2021-08-21 24 0.002414
2021-02-01 23 0.002314
2022-01-03 23 0.002314
2022-08-18 23 0.002314
2022-08-27 21 0.002113
2020-12-20 21 0.002113
2022-03-28 20 0.002012
2021-12-26 20 0.002012
2022-11-20 20 0.002012
2022-08-25 20 0.002012
2020-11-07 20 0.002012
2022-03-21 20 0.002012
2020-10-07 19 0.001911
2020-12-09 19 0.001911
2023-02-04 19 0.001911
2021-11-02 18 0.001811
2022-08-19 18 0.001811
2022-08-30 18 0.001811
2020-11-01 18 0.001811
2022-09-01 18 0.001811
2021-05-07 18 0.001811
2022-07-06 18 0.001811
2022-10-05 17 0.001710
2023-02-20 16 0.001610
2021-07-04 16 0.001610
2022-12-18 16 0.001610
2021-01-24 16 0.001610
2021-12-20 16 0.001610
2021-01-30 16 0.001610
2023-01-01 16 0.001610
2022-01-24 16 0.001610
2023-02-19 16 0.001610
2022-08-23 15 0.001509
2020-11-09 15 0.001509
2022-12-19 15 0.001509
2021-02-22 15 0.001509
2022-02-07 15 0.001509
2021-12-07 15 0.001509
2022-11-27 15 0.001509
2022-05-05 14 0.001408
2021-04-26 14 0.001408
2021-01-11 14 0.001408
2021-02-07 14 0.001408
2022-12-05 14 0.001408
2022-04-16 13 0.001308
2021-03-29 13 0.001308
2022-09-05 13 0.001308
2022-02-27 12 0.001207
2022-08-20 12 0.001207
2022-12-09 12 0.001207
2021-04-04 12 0.001207
2021-12-27 12 0.001207
2020-04-11 12 0.001207
2020-12-07 12 0.001207
2022-02-04 12 0.001207
2021-08-09 12 0.001207
2021-01-17 12 0.001207
2021-01-25 12 0.001207
2020-11-03 11 0.001107
2020-12-26 11 0.001107
2022-08-26 11 0.001107
2021-05-10 11 0.001107
2021-01-10 11 0.001107
2022-12-07 11 0.001107
2020-10-26 11 0.001107
2022-01-17 11 0.001107
2021-08-16 10 0.001006
2022-11-21 10 0.001006
2020-12-28 10 0.001006
2021-06-14 10 0.001006
2022-10-31 10 0.001006
2021-03-07 10 0.001006
2020-11-29 10 0.001006
2020-12-27 10 0.001006
2022-12-25 10 0.001006
2021-02-08 10 0.001006
2020-10-02 10 0.001006
2021-02-21 10 0.001006
2021-05-24 10 0.001006
2021-06-07 10 0.001006
2021-06-06 10 0.001006
2020-11-23 10 0.001006
2022-01-23 10 0.001006
2022-01-31 10 0.001006
2022-04-11 10 0.001006
2021-08-22 10 0.001006
2020-11-30 10 0.001006
2020-10-18 10 0.001006
2023-01-30 9 0.000905
2021-06-20 9 0.000905
2021-06-27 9 0.000905
2020-12-13 9 0.000905
2022-12-26 9 0.000905
2020-11-16 9 0.000905
2022-08-16 9 0.000905
2021-02-14 9 0.000905
2021-12-12 8 0.000805
2022-12-11 8 0.000805
2020-11-22 8 0.000805
2023-05-02 8 0.000805
2023-06-12 8 0.000805
2022-05-15 8 0.000805
2022-02-13 8 0.000805
2022-07-17 8 0.000805
2023-01-08 8 0.000805
2021-11-29 8 0.000805
2023-01-23 8 0.000805
2022-01-16 8 0.000805
2023-04-08 8 0.000805
2021-09-13 8 0.000805
2021-04-19 8 0.000805
2021-03-08 8 0.000805
2021-02-15 8 0.000805
2021-05-03 8 0.000805
2021-04-25 8 0.000805
2021-08-08 8 0.000805
2022-05-01 8 0.000805
2021-09-12 8 0.000805
2021-04-12 8 0.000805
2021-04-18 7 0.000704
2021-03-22 7 0.000704
2021-05-31 7 0.000704
2023-04-30 7 0.000704
2023-02-27 7 0.000704
2021-08-05 7 0.000704
2021-10-31 7 0.000704
2023-04-09 7 0.000704
2021-03-15 7 0.000704
2022-04-04 7 0.000704
2021-04-05 7 0.000704
2021-12-09 7 0.000704
2021-11-15 7 0.000704
2022-06-19 7 0.000704
2021-10-10 7 0.000704
2021-08-23 7 0.000704
2022-11-13 7 0.000704
2021-06-21 7 0.000704
2022-10-30 7 0.000704
2021-02-28 7 0.000704
2021-01-18 7 0.000704
2021-10-17 7 0.000704
2022-06-26 7 0.000704
2023-04-03 7 0.000704
2021-11-01 6 0.000604
2023-01-15 6 0.000604
2022-11-07 6 0.000604
2021-05-17 6 0.000604
2022-06-06 6 0.000604
2021-12-13 6 0.000604
2021-04-11 6 0.000604
2022-05-23 6 0.000604
2021-11-22 6 0.000604
2022-04-18 6 0.000604
2021-08-15 6 0.000604
2023-03-13 6 0.000604
2023-01-09 6 0.000604
2021-11-14 6 0.000604
2022-05-16 6 0.000604
2022-07-18 6 0.000604
2020-11-15 6 0.000604
2021-03-21 6 0.000604
2023-07-09 6 0.000604
2021-10-13 6 0.000604
2023-06-05 6 0.000604
2022-02-21 6 0.000604
2021-10-25 6 0.000604
2021-07-11 5 0.000503
2021-11-21 5 0.000503
2022-12-12 5 0.000503
2023-07-03 5 0.000503
2022-06-13 5 0.000503
2021-09-26 5 0.000503
2022-08-08 5 0.000503
2021-06-13 5 0.000503
2023-06-19 5 0.000503
2022-05-09 5 0.000503
2023-04-23 5 0.000503
2023-05-22 5 0.000503
2021-06-28 5 0.000503
2020-10-13 5 0.000503
2023-04-17 5 0.000503
2021-10-18 5 0.000503
2021-05-14 5 0.000503
2023-02-26 5 0.000503
2023-01-29 5 0.000503
2020-09-28 5 0.000503
2021-08-29 4 0.000402
2021-05-23 4 0.000402
2021-07-25 4 0.000402
2020-10-19 4 0.000402
2021-08-30 4 0.000402
2022-06-20 4 0.000402
2022-02-20 4 0.000402
2022-02-14 4 0.000402
2022-05-22 4 0.000402
2021-07-12 4 0.000402
2021-05-09 4 0.000402
2021-07-05 4 0.000402
2022-06-12 4 0.000402
2021-07-19 4 0.000402
2021-05-30 4 0.000402
2022-07-25 4 0.000402
2022-09-19 4 0.000402
2022-09-11 4 0.000402
2023-05-15 4 0.000402
2023-07-02 4 0.000402
2023-01-16 4 0.000402
2022-07-31 4 0.000402
2021-09-27 4 0.000402
2021-10-11 4 0.000402
2022-04-10 4 0.000402
2021-10-24 4 0.000402
2023-05-28 4 0.000402
2022-09-12 4 0.000402
2022-08-15 4 0.000402
2023-03-12 4 0.000402
2022-09-25 4 0.000402
2022-07-24 4 0.000402
2023-03-27 4 0.000402
2022-08-29 4 0.000402
2022-10-17 4 0.000402
2022-10-24 4 0.000402
2023-03-20 3 0.000302
2022-05-29 3 0.000302
2023-05-01 3 0.000302
2022-09-26 3 0.000302
2022-04-25 3 0.000302
2022-04-17 3 0.000302
2021-09-05 3 0.000302
2022-11-28 3 0.000302
2023-03-19 3 0.000302
2022-10-23 3 0.000302
2023-06-26 3 0.000302
2021-09-06 3 0.000302
2022-08-14 3 0.000302
2023-04-24 3 0.000302
2021-12-06 3 0.000302
2021-11-28 3 0.000302
2021-03-14 3 0.000302
2023-06-18 2 0.000201
2022-07-04 2 0.000201
2021-07-18 2 0.000201
2021-07-26 2 0.000201
2022-05-02 2 0.000201
2021-11-08 2 0.000201
2020-10-11 2 0.000201
2021-09-19 2 0.000201
2022-08-01 2 0.000201
2023-04-10 2 0.000201
2023-05-29 2 0.000201
2022-08-21 2 0.000201
2022-08-22 2 0.000201
2023-01-22 2 0.000201
2022-05-30 2 0.000201
2021-09-20 2 0.000201
2021-07-06 2 0.000201
2022-10-16 2 0.000201
2023-04-16 2 0.000201
2022-11-02 1 0.000101
2022-01-04 1 0.000101
2022-10-13 1 0.000101
2022-08-28 1 0.000101
2022-11-14 1 0.000101
2021-10-02 1 0.000101
2022-04-24 1 0.000101
2022-05-18 1 0.000101
2022-09-18 1 0.000101
2023-06-11 1 0.000101
2023-05-14 1 0.000101
2023-05-21 1 0.000101
2022-06-27 1 0.000101
2023-07-04 1 0.000101
2021-08-03 1 0.000101
msf_fechacambiolevelrelacion__c: Fecha de cambio de nivel de relación.
Se puede observar que aunque practicamente no hay vacios ni nulos, el 85% de la muestra tiene fecha
Analsis de distribución por variables
-> msf_datefirstdonation__c: Variable fecha
In [622]:
# Vamos a realizar analisis por cada variable
var = "msf_datefirstdonation__c"
In [623]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datefirstdonation__c es 727687. Lo que supone un 73.20323439939482%
El nº de vacios para la variable msf_datefirstdonation__c es 0. Lo que supone un 0.0%
Out[623]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c']
In [624]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[624]:
# Tot % Tot
2017-12-01 4910 1.843252
2010-02-01 4854 1.822229
2020-07-01 4209 1.580091
2014-11-01 3026 1.135984
2003-08-01 2919 1.095815
... ... ...
1994-06-28 1 0.000375
2008-08-26 1 0.000375
2002-02-09 1 0.000375
2011-03-31 1 0.000375
2012-02-05 1 0.000375

9586 rows × 2 columns

msf_datefirstdonation__c: Fecha de la primera donacion.
Se puede observar que hay más de un 73% de los registros a vacio.
Analsis de distribución por variables
-> msf_datefirstrecurringdonorquota__c: Variable fecha
In [625]:
# Vamos a realizar analisis por cada variable
var = "msf_datefirstrecurringdonorquota__c"
In [626]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datefirstrecurringdonorquota__c es 48715. Lo que supone un 4.9005899016562315%
El nº de vacios para la variable msf_datefirstrecurringdonorquota__c es 0. Lo que supone un 0.0%
In [627]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[627]:
# Tot % Tot
2006-01-05 9932 1.050617
2011-01-03 9921 1.049454
2009-01-02 9654 1.021210
2021-01-05 8946 0.946317
2014-11-03 8567 0.906226
2015-01-02 8523 0.901572
2005-02-04 8472 0.896177
2014-12-02 8309 0.878935
2005-01-04 8185 0.865818
2012-01-02 8034 0.849845
2016-07-01 7977 0.843815
2004-02-01 7780 0.822976
2004-01-01 7525 0.796002
2015-10-01 7445 0.787540
2007-01-04 6987 0.739092
2016-01-04 6778 0.716984
2017-04-03 6649 0.703338
2010-01-04 6590 0.697097
2003-01-01 6482 0.685673
2015-11-03 6357 0.672450
2013-01-02 6285 0.664834
2016-04-01 6213 0.657218
2015-12-02 6209 0.656794
2003-03-01 6125 0.647909
2017-06-01 6057 0.640716
2016-08-01 6024 0.637225
2015-03-02 5930 0.627282
2015-02-02 5913 0.625483
2014-05-05 5836 0.617338
2016-10-03 5733 0.606443
2016-12-01 5723 0.605385
2015-04-01 5646 0.597240
2015-05-04 5559 0.588037
2017-07-03 5550 0.587085
2016-05-02 5498 0.581584
2016-11-02 5485 0.580209
2017-12-04 5456 0.577141
2010-02-01 5447 0.576189
2016-06-01 5400 0.571218
2017-01-02 5374 0.568467
2015-06-02 5328 0.563601
2006-02-03 5327 0.563496
2016-03-01 5319 0.562649
2017-08-01 5305 0.561168
2017-03-02 5278 0.558312
2009-02-03 5192 0.549215
2016-02-01 5139 0.543609
2014-01-02 5138 0.543503
2015-08-03 5125 0.542128
2014-08-01 4955 0.524145
2017-05-02 4884 0.516635
2014-06-05 4880 0.516211
2015-07-01 4823 0.510182
2017-02-02 4793 0.507009
2018-01-03 4793 0.507009
2009-12-02 4788 0.506480
2018-02-01 4744 0.501825
2018-06-01 4697 0.496854
2010-12-02 4667 0.493680
2018-03-01 4629 0.489660
2014-10-02 4554 0.481727
2014-04-02 4549 0.481198
2000-02-01 4504 0.476438
2012-02-01 4426 0.468187
2007-02-05 4376 0.462898
2013-02-01 4356 0.460782
2017-09-01 4342 0.459301
2011-12-01 4336 0.458667
2014-07-02 4277 0.452426
2018-07-02 4214 0.445761
2017-11-02 4202 0.444492
2014-02-03 4167 0.440790
2022-04-02 4157 0.439732
2018-08-01 4146 0.438568
2018-12-03 4109 0.434654
2011-02-01 4044 0.427779
2017-10-02 4002 0.423336
2018-04-03 3975 0.420480
2018-11-02 3934 0.416143
2008-12-01 3868 0.409161
2013-12-02 3847 0.406940
2005-03-04 3824 0.404507
2013-11-04 3805 0.402497
2019-12-02 3789 0.400804
2008-01-03 3781 0.399958
2020-02-03 3769 0.398689
2000-01-01 3719 0.393400
2014-03-03 3718 0.393294
2019-01-02 3698 0.391178
1994-10-01 3689 0.390226
2008-02-04 3668 0.388005
2019-11-04 3644 0.385466
2016-09-01 3614 0.382293
2011-04-01 3614 0.382293
2006-12-02 3592 0.379965
2020-03-02 3576 0.378273
2019-06-03 3564 0.377004
2021-04-02 3550 0.375523
2021-03-02 3533 0.373724
2022-12-02 3531 0.373513
2020-01-02 3525 0.372878
2018-05-03 3520 0.372349
2013-05-02 3516 0.371926
2014-09-03 3514 0.371715
2021-07-02 3491 0.369282
2019-08-01 3489 0.369070
2013-08-02 3472 0.367272
2019-05-02 3458 0.365791
2019-04-01 3450 0.364945
2021-06-02 3422 0.361983
2019-07-01 3411 0.360819
2023-03-02 3388 0.358386
2022-07-05 3378 0.357328
2019-02-01 3371 0.356588
2013-06-03 3351 0.354472
2001-03-01 3338 0.353097
2011-03-01 3329 0.352145
1995-02-01 3309 0.350029
2023-04-04 3299 0.348972
2011-08-02 3284 0.347385
2018-10-02 3267 0.345587
2012-12-03 3224 0.341038
2023-06-02 3214 0.339980
2013-03-01 3173 0.335643
2019-03-01 3145 0.332681
2007-12-02 3138 0.331941
2013-04-02 3135 0.331624
2022-11-03 3133 0.331412
2019-10-02 3129 0.330989
2012-11-02 3101 0.328027
2015-09-01 3095 0.327392
2023-07-04 3090 0.326863
2022-06-02 3090 0.326863
2021-10-02 3081 0.325911
2013-07-01 3057 0.323373
2001-02-01 3052 0.322844
2021-12-02 3037 0.321257
2010-08-02 3034 0.320940
2021-05-04 3031 0.320622
2023-02-02 3003 0.317660
2004-03-01 2977 0.314910
2005-12-03 2966 0.313747
2022-10-04 2932 0.310150
2012-08-01 2918 0.308669
2021-11-03 2889 0.305601
2022-01-04 2796 0.295764
2022-08-02 2788 0.294918
1998-03-01 2785 0.294600
2010-03-01 2774 0.293437
2009-03-03 2766 0.292590
2011-11-02 2764 0.292379
2013-10-02 2744 0.290263
2018-09-03 2741 0.289946
2021-08-03 2733 0.289100
2023-01-03 2726 0.288359
2012-03-01 2704 0.286032
1999-01-01 2671 0.282541
2021-02-02 2665 0.281906
2022-03-02 2663 0.281695
1994-02-01 2653 0.280637
2012-06-04 2649 0.280214
2022-05-03 2647 0.280002
2012-04-02 2627 0.277887
2011-05-02 2597 0.274713
2013-09-02 2590 0.273973
2022-02-02 2538 0.268472
2008-08-08 2535 0.268155
2020-04-02 2480 0.262337
2002-01-01 2476 0.261914
2010-04-01 2454 0.259587
2012-07-02 2451 0.259269
2011-07-01 2432 0.257259
2006-11-03 2415 0.255461
2011-06-01 2406 0.254509
2010-07-01 2364 0.250066
2008-04-04 2363 0.249961
2023-05-03 2331 0.246576
2020-05-03 2326 0.246047
2007-04-02 2315 0.244883
2007-03-02 2312 0.244566
2011-09-02 2278 0.240969
2011-10-04 2247 0.237690
2002-12-01 2244 0.237373
1999-02-01 2212 0.233988
2012-05-03 2203 0.233036
2008-03-03 2103 0.222458
2006-03-03 2090 0.221082
2009-04-02 2086 0.220659
2009-07-02 2080 0.220025
2012-10-01 2029 0.214630
2008-06-02 2025 0.214207
2010-06-02 2020 0.213678
2004-12-05 2000 0.211562
1996-02-01 1978 0.209235
2006-04-03 1971 0.208494
2007-10-04 1948 0.206061
2019-09-02 1913 0.202359
2001-01-01 1885 0.199397
2007-05-04 1875 0.198339
2007-09-03 1874 0.198234
2010-10-04 1874 0.198234
2007-07-04 1851 0.195801
2020-08-03 1830 0.193579
2005-11-03 1805 0.190935
1994-07-01 1800 0.190406
2003-12-01 1795 0.189877
2010-05-03 1767 0.186915
2005-08-02 1758 0.185963
2010-11-02 1716 0.181520
2009-08-03 1714 0.181309
2009-06-04 1707 0.180568
2009-10-02 1699 0.179722
2008-07-04 1689 0.178664
2009-05-04 1670 0.176654
2005-06-03 1660 0.175597
2006-06-02 1649 0.174433
1997-02-01 1647 0.174221
2008-05-02 1629 0.172317
2020-06-02 1626 0.172000
2005-07-04 1625 0.171894
2007-08-02 1584 0.167557
1998-02-01 1546 0.163537
2006-07-03 1528 0.161633
2009-09-02 1514 0.160152
2008-09-01 1513 0.160047
2009-11-02 1506 0.159306
1995-04-01 1480 0.156556
2000-03-01 1436 0.151902
2007-11-02 1434 0.151690
1994-01-01 1422 0.150421
2012-09-03 1422 0.150421
2022-09-02 1403 0.148411
2021-09-02 1394 0.147459
2020-07-01 1384 0.146401
2005-04-04 1365 0.144391
1992-11-01 1352 0.143016
1998-01-01 1348 0.142593
1994-09-01 1327 0.140371
2008-10-02 1293 0.136775
2008-11-03 1292 0.136669
2010-09-02 1286 0.136034
2005-05-04 1242 0.131380
2007-06-05 1226 0.129688
2020-09-01 1223 0.129370
2005-09-02 1180 0.124822
1995-03-01 1169 0.123658
2006-08-02 1121 0.118581
1999-06-01 1076 0.113820
2005-10-03 1055 0.111599
2006-05-04 973 0.102925
1999-03-01 960 0.101550
1997-01-01 930 0.098376
2002-04-01 880 0.093087
2002-02-01 878 0.092876
2004-04-01 875 0.092558
1994-03-01 866 0.091606
2006-09-04 862 0.091183
2003-11-01 850 0.089914
1995-01-01 839 0.088750
2003-06-01 830 0.087798
2006-10-02 826 0.087375
1995-07-01 821 0.086846
2003-04-01 795 0.084096
2002-05-01 780 0.082509
2004-11-04 769 0.081346
2001-04-01 756 0.079970
2000-05-01 744 0.078701
1999-07-01 710 0.075105
1994-04-01 691 0.073095
2003-08-01 683 0.072248
2004-05-01 667 0.070556
2004-06-01 662 0.070027
1992-12-01 662 0.070027
1995-10-01 661 0.069921
1996-04-01 651 0.068863
1999-05-01 643 0.068017
2000-04-01 634 0.067065
2001-08-01 617 0.065267
2004-08-01 609 0.064421
1999-12-01 591 0.062517
2020-12-02 589 0.062305
1998-04-01 577 0.061036
1996-12-01 573 0.060613
2003-05-01 555 0.058708
1998-09-01 549 0.058074
1998-12-01 531 0.056170
1994-06-01 524 0.055429
2000-01-13 516 0.054583
2001-12-01 513 0.054266
1996-01-01 510 0.053948
2001-07-01 505 0.053419
2004-10-06 499 0.052785
1998-11-01 492 0.052044
2004-07-01 490 0.051833
1993-11-01 489 0.051727
2002-11-01 485 0.051304
1996-03-01 477 0.050458
1996-06-01 476 0.050352
1995-06-01 466 0.049294
2002-08-01 463 0.048977
2003-10-01 455 0.048130
1998-05-01 447 0.047284
1992-06-01 445 0.047073
1993-01-01 415 0.043899
1993-03-01 412 0.043582
1993-07-01 404 0.042736
2002-03-01 402 0.042524
1997-03-01 398 0.042101
1996-07-01 379 0.040091
1998-06-01 377 0.039879
2004-09-03 370 0.039139
1994-08-01 369 0.039033
2000-06-01 366 0.038716
1993-02-01 354 0.037446
1999-04-01 343 0.036283
2020-11-04 340 0.035966
1994-05-01 332 0.035119
2002-09-01 332 0.035119
1994-12-01 329 0.034802
2003-09-01 329 0.034802
1993-12-01 325 0.034379
2003-02-01 323 0.034167
2000-07-01 320 0.033850
1997-11-01 312 0.033004
1995-05-01 297 0.031417
2003-07-01 287 0.030359
2002-10-01 279 0.029513
1999-08-01 278 0.029407
1995-12-01 269 0.028455
2001-11-01 263 0.027820
1995-11-01 259 0.027397
1995-09-01 246 0.026022
1998-08-01 245 0.025916
1994-11-01 240 0.025387
2001-05-01 237 0.025070
2020-10-02 233 0.024647
1997-12-01 228 0.024118
1992-08-01 225 0.023801
1998-07-01 224 0.023695
1996-05-01 223 0.023589
1994-01-11 219 0.023166
1997-06-01 216 0.022849
1996-08-01 198 0.020945
2000-04-05 197 0.020839
1997-05-01 196 0.020733
2001-09-01 196 0.020733
1993-06-01 195 0.020627
1996-09-01 195 0.020627
2001-10-01 192 0.020310
1993-10-01 189 0.019993
1992-07-01 185 0.019569
1993-05-01 184 0.019464
2000-12-01 179 0.018935
1997-04-01 174 0.018406
1997-07-01 166 0.017560
2000-08-01 157 0.016608
1996-11-01 151 0.015973
1999-09-01 150 0.015867
1999-11-01 150 0.015867
1999-10-01 147 0.015550
2017-07-01 144 0.015232
2000-03-09 141 0.014915
2015-01-01 140 0.014809
2017-01-01 140 0.014809
2014-12-01 139 0.014704
1993-04-01 139 0.014704
1995-08-01 138 0.014598
2015-02-01 137 0.014492
2002-06-17 134 0.014175
2001-06-01 129 0.013646
2000-11-01 128 0.013540
1996-10-01 126 0.013328
2015-12-01 126 0.013328
2015-06-01 124 0.013117
2000-09-01 123 0.013011
1991-01-20 120 0.012694
1992-09-01 120 0.012694
2014-01-01 118 0.012482
2017-05-01 117 0.012376
1997-08-01 117 0.012376
1992-10-01 115 0.012165
2015-05-01 115 0.012165
2013-12-01 115 0.012165
2015-03-01 115 0.012165
2000-10-01 114 0.012059
2002-06-12 112 0.011847
1993-08-01 112 0.011847
2016-01-01 108 0.011424
2016-05-01 107 0.011319
2021-02-05 106 0.011213
1998-10-01 106 0.011213
1997-09-01 103 0.010895
2002-06-13 102 0.010790
2015-08-01 101 0.010684
2014-07-01 98 0.010367
2014-05-01 97 0.010261
2015-11-01 97 0.010261
2014-09-01 94 0.009943
2017-02-01 91 0.009626
2017-04-01 91 0.009626
2018-01-01 88 0.009309
2010-12-01 88 0.009309
2016-11-01 87 0.009203
2018-07-01 87 0.009203
2017-03-01 87 0.009203
2011-05-01 86 0.009097
2014-06-01 85 0.008991
2014-11-01 84 0.008886
2009-02-01 82 0.008674
2020-03-01 82 0.008674
2014-03-01 81 0.008568
2012-07-01 81 0.008568
2013-09-01 81 0.008568
2018-12-01 80 0.008462
2013-08-01 80 0.008462
1993-09-01 78 0.008251
2018-04-01 77 0.008145
2014-04-01 77 0.008145
2012-04-01 76 0.008039
2002-06-07 76 0.008039
2011-01-01 74 0.007828
2014-10-01 74 0.007828
2010-09-01 74 0.007828
1997-10-01 73 0.007722
2017-12-01 73 0.007722
2010-05-01 71 0.007510
2012-12-01 71 0.007510
1991-02-20 71 0.007510
2017-10-01 70 0.007405
2012-05-01 70 0.007405
2002-06-14 69 0.007299
2002-06-11 68 0.007193
2013-11-01 68 0.007193
2013-10-01 67 0.007087
2013-06-01 67 0.007087
2017-11-01 66 0.006982
2016-10-01 65 0.006876
2018-09-01 65 0.006876
2002-06-06 65 0.006876
2002-07-05 64 0.006770
2008-02-01 64 0.006770
2013-01-01 63 0.006664
2011-09-01 63 0.006664
2014-02-01 63 0.006664
2012-01-01 62 0.006558
2018-05-01 61 0.006453
2012-09-01 60 0.006347
2019-12-01 60 0.006347
2020-02-01 59 0.006241
2012-06-01 58 0.006135
2011-08-01 58 0.006135
2013-05-01 57 0.006030
2010-01-01 56 0.005924
2008-03-01 56 0.005924
2002-06-19 56 0.005924
1991-12-01 54 0.005712
2019-06-01 54 0.005712
2010-11-01 53 0.005606
2013-04-01 53 0.005606
2007-02-01 53 0.005606
2002-06-10 52 0.005501
2010-08-01 52 0.005501
1992-01-02 51 0.005395
2018-11-01 51 0.005395
2019-11-01 51 0.005395
2019-01-01 51 0.005395
2008-04-01 50 0.005289
2019-09-01 50 0.005289
2008-06-01 50 0.005289
2009-03-01 49 0.005183
2020-04-01 49 0.005183
2011-11-01 47 0.004972
1991-08-01 46 0.004866
2019-05-01 46 0.004866
2020-01-01 45 0.004760
1991-01-21 43 0.004549
1991-11-15 42 0.004443
2012-11-01 42 0.004443
1991-07-01 42 0.004443
2009-09-01 41 0.004337
2008-10-01 40 0.004231
2008-07-01 40 0.004231
2009-01-01 40 0.004231
1991-11-06 40 0.004231
2010-06-01 39 0.004125
2009-08-01 39 0.004125
1991-11-01 39 0.004125
2009-04-01 38 0.004020
2018-10-01 38 0.004020
2007-03-01 38 0.004020
2005-09-01 37 0.003914
1991-11-11 36 0.003808
2009-10-01 35 0.003702
2011-10-01 35 0.003702
2008-01-01 34 0.003597
2009-06-01 33 0.003491
2009-11-01 33 0.003491
2007-01-01 33 0.003491
2006-05-01 33 0.003491
2002-06-18 33 0.003491
2006-04-01 32 0.003385
1992-03-02 32 0.003385
2020-05-01 32 0.003385
2002-07-04 32 0.003385
2009-12-01 32 0.003385
2005-12-01 32 0.003385
2005-08-01 31 0.003279
2006-12-01 30 0.003173
2002-06-28 30 0.003173
2007-10-01 30 0.003173
2007-06-01 29 0.003068
2008-05-01 29 0.003068
2008-08-01 29 0.003068
2007-04-01 29 0.003068
2002-06-21 28 0.002962
1992-06-02 28 0.002962
2009-05-01 27 0.002856
1992-02-02 26 0.002750
2006-07-01 26 0.002750
1991-06-03 25 0.002645
1992-01-14 25 0.002645
1995-01-02 25 0.002645
2007-12-01 25 0.002645
1994-10-06 25 0.002645
2010-10-01 24 0.002539
1991-10-01 24 0.002539
2005-10-01 24 0.002539
1994-03-28 23 0.002433
2007-11-01 23 0.002433
2009-07-01 23 0.002433
2006-08-01 23 0.002433
2007-08-01 23 0.002433
1991-03-25 22 0.002327
2019-10-01 22 0.002327
1995-10-02 22 0.002327
2006-06-01 22 0.002327
2006-10-01 20 0.002116
2005-11-01 20 0.002116
2006-09-01 19 0.002010
2007-09-01 19 0.002010
2007-05-01 19 0.002010
1993-04-05 16 0.001692
2008-11-01 15 0.001587
1992-02-01 14 0.001481
1991-09-01 13 0.001375
1993-11-08 13 0.001375
1994-07-04 13 0.001375
2007-07-01 13 0.001375
1993-10-04 13 0.001375
1996-01-02 12 0.001269
1994-03-04 12 0.001269
1992-04-01 12 0.001269
2006-11-01 12 0.001269
1993-12-07 11 0.001164
1994-01-07 11 0.001164
2002-07-03 10 0.001058
2006-01-01 10 0.001058
1992-12-14 10 0.001058
1992-09-30 10 0.001058
1993-01-07 9 0.000952
1993-09-16 8 0.000846
1993-03-11 8 0.000846
1991-05-16 7 0.000740
2020-06-01 7 0.000740
2002-07-01 6 0.000635
2021-12-01 6 0.000635
1992-01-16 6 0.000635
1995-09-07 5 0.000529
1994-02-07 5 0.000529
1996-09-02 5 0.000529
1994-06-06 5 0.000529
2021-06-01 5 0.000529
1993-02-12 5 0.000529
2002-06-05 5 0.000529
1993-08-05 5 0.000529
1991-01-10 4 0.000423
1990-12-10 4 0.000423
2020-08-01 4 0.000423
1995-05-02 4 0.000423
2021-07-01 4 0.000423
2021-11-02 4 0.000423
2021-04-01 3 0.000317
2002-06-27 3 0.000317
2002-06-25 3 0.000317
2002-06-26 3 0.000317
1991-04-02 3 0.000317
1990-10-01 3 0.000317
2022-11-02 3 0.000317
1991-01-01 3 0.000317
2020-10-05 3 0.000317
1991-05-08 3 0.000317
1992-09-25 3 0.000317
2002-07-02 3 0.000317
1992-10-14 3 0.000317
1996-08-02 3 0.000317
1993-04-12 3 0.000317
1996-09-30 2 0.000212
1993-02-17 2 0.000212
1994-12-04 2 0.000212
1995-10-21 2 0.000212
1997-05-04 2 0.000212
2002-05-28 2 0.000212
1999-12-28 2 0.000212
1998-08-18 2 0.000212
2002-06-01 2 0.000212
2021-08-02 2 0.000212
1990-03-01 2 0.000212
1990-02-20 2 0.000212
1990-09-25 2 0.000212
1998-08-21 2 0.000212
1993-02-09 2 0.000212
1994-08-19 2 0.000212
1998-12-18 2 0.000212
1991-10-25 2 0.000212
1996-12-21 2 0.000212
2022-01-03 2 0.000212
2002-06-03 2 0.000212
1993-07-05 2 0.000212
2002-07-19 1 0.000106
2002-07-12 1 0.000106
2002-07-28 1 0.000106
2002-06-04 1 0.000106
2002-07-10 1 0.000106
1992-09-23 1 0.000106
1992-05-18 1 0.000106
2023-06-01 1 0.000106
1996-09-05 1 0.000106
1991-03-15 1 0.000106
1996-06-03 1 0.000106
2021-02-01 1 0.000106
1991-10-21 1 0.000106
1990-01-01 1 0.000106
1994-10-10 1 0.000106
1992-09-19 1 0.000106
1996-07-12 1 0.000106
1994-07-28 1 0.000106
1999-04-22 1 0.000106
2021-05-03 1 0.000106
1996-03-20 1 0.000106
1996-12-26 1 0.000106
2000-02-14 1 0.000106
2000-03-30 1 0.000106
1991-06-01 1 0.000106
2014-04-05 1 0.000106
2000-04-26 1 0.000106
1990-01-10 1 0.000106
1991-01-08 1 0.000106
1994-01-10 1 0.000106
1991-04-01 1 0.000106
1996-12-28 1 0.000106
1997-06-04 1 0.000106
1992-08-14 1 0.000106
1992-05-15 1 0.000106
1990-04-20 1 0.000106
1994-02-14 1 0.000106
1991-09-03 1 0.000106
1991-01-31 1 0.000106
1992-03-24 1 0.000106
1995-07-19 1 0.000106
1992-06-10 1 0.000106
1992-04-02 1 0.000106
1990-11-01 1 0.000106
1996-09-25 1 0.000106
2002-07-17 1 0.000106
2002-07-27 1 0.000106
1993-04-03 1 0.000106
1994-10-11 1 0.000106
1990-01-20 1 0.000106
1993-02-15 1 0.000106
1999-03-25 1 0.000106
1993-05-04 1 0.000106
1993-02-05 1 0.000106
1994-09-10 1 0.000106
1993-02-04 1 0.000106
1993-02-08 1 0.000106
1997-04-02 1 0.000106
1999-08-12 1 0.000106
2000-03-28 1 0.000106
1990-06-10 1 0.000106
1991-04-23 1 0.000106
1998-10-20 1 0.000106
1992-03-01 1 0.000106
1993-04-19 1 0.000106
1993-03-10 1 0.000106
1991-05-05 1 0.000106
1998-02-05 1 0.000106
1999-10-25 1 0.000106
1991-04-04 1 0.000106
1998-02-09 1 0.000106
2022-06-01 1 0.000106
1993-04-30 1 0.000106
1999-10-27 1 0.000106
1995-10-11 1 0.000106
1990-03-31 1 0.000106
1992-05-21 1 0.000106
1995-10-26 1 0.000106
1996-11-09 1 0.000106
1992-09-29 1 0.000106
1991-08-03 1 0.000106
1992-11-02 1 0.000106
1991-03-20 1 0.000106
2022-03-09 1 0.000106
2022-05-02 1 0.000106
2023-04-03 1 0.000106
2022-07-04 1 0.000106
1992-05-22 1 0.000106
1993-03-08 1 0.000106
1991-06-20 1 0.000106
2020-12-01 1 0.000106
1991-01-04 1 0.000106
1990-02-12 1 0.000106
1991-12-03 1 0.000106
1996-12-20 1 0.000106
1992-06-08 1 0.000106
1996-12-13 1 0.000106
1992-11-19 1 0.000106
1993-03-16 1 0.000106
1992-02-03 1 0.000106
1996-01-30 1 0.000106
msf_datefirstrecurringdonorquota__c: Fecha de la primera donacion recurrente.
Se puede observar que apenas hay vacios, un 5%. Se puede usar para tener variables como tiempo desde primera donacion recurrente o tiempo hasta modificaicon de cuota.
Analsis de distribución por variables
-> msf_datelastrecurringdonorquota__c: Variable fecha
In [628]:
# Vamos a realizar analisis por cada variable
var = "msf_datelastrecurringdonorquota__c"
In [629]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datelastrecurringdonorquota__c es 48715. Lo que supone un 4.9005899016562315%
El nº de vacios para la variable msf_datelastrecurringdonorquota__c es 0. Lo que supone un 0.0%
In [630]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[630]:
# Tot % Tot
2023-07-04 393458 41.620396
2023-06-02 22387 2.368120
2023-05-03 21046 2.226268
2023-01-03 14510 1.534883
2023-02-02 13748 1.454278
2023-03-02 11212 1.186017
2022-12-02 10506 1.111336
2023-04-04 9691 1.025124
2022-11-03 8475 0.896494
2022-10-04 7566 0.800339
2022-08-02 7167 0.758133
2020-09-01 6699 0.708627
2022-09-02 6368 0.673614
2018-02-01 5264 0.556831
2018-01-03 4175 0.441636
2021-04-02 3879 0.410325
2022-01-04 3867 0.409055
2017-12-04 3853 0.407574
2019-01-02 3847 0.406940
2018-12-03 3757 0.397419
2021-01-05 3666 0.387793
2019-12-02 3651 0.386207
2021-12-02 3630 0.383985
2021-07-02 3591 0.379860
2021-10-02 3581 0.378802
2018-03-01 3569 0.377533
2021-11-03 3438 0.363675
2021-06-02 3421 0.361877
2022-07-05 3410 0.360713
2022-05-03 3400 0.359656
2021-03-02 3396 0.359232
2021-05-04 3395 0.359127
2018-09-03 3323 0.351510
2022-02-02 3290 0.348020
2021-02-02 3277 0.346644
2018-10-02 3275 0.346433
2020-01-02 3258 0.344635
2022-04-02 3253 0.344106
2018-04-03 3230 0.341673
2018-07-02 3223 0.340932
2022-06-02 3201 0.338605
2019-02-01 3144 0.332576
2021-08-03 3143 0.332470
2020-02-03 3126 0.330672
2017-10-02 3114 0.329402
2019-10-02 3114 0.329402
2018-08-01 3107 0.328662
2020-03-02 3099 0.327815
2019-08-01 3098 0.327710
2022-03-02 3095 0.327392
2018-06-01 3075 0.325277
2019-09-02 3063 0.324007
2017-11-02 3032 0.320728
2018-11-02 3027 0.320199
2019-07-01 3024 0.319882
2019-03-01 2966 0.313747
2017-01-02 2947 0.311737
2016-12-01 2909 0.307717
2018-05-03 2882 0.304861
2017-09-01 2868 0.303380
2019-04-01 2811 0.297351
2019-11-04 2788 0.294918
2021-09-02 2681 0.283599
2019-05-02 2640 0.279262
2017-08-01 2596 0.274608
2019-06-03 2538 0.268472
2017-07-03 2517 0.266251
2015-12-02 2422 0.256202
2016-10-03 2399 0.253769
2017-02-02 2394 0.253240
2017-03-02 2383 0.252076
2017-05-02 2360 0.249643
2020-04-02 2339 0.247422
2017-06-01 2336 0.247105
2016-02-01 2269 0.240017
2017-04-03 2269 0.240017
2016-11-02 2265 0.239594
2012-12-03 2252 0.238219
2016-01-04 2217 0.234517
2016-09-01 2209 0.233670
2020-07-01 2190 0.231660
2015-01-02 2138 0.226160
2012-01-02 2137 0.226054
2014-01-02 2131 0.225419
2014-12-02 2112 0.223410
2020-05-03 2102 0.222352
2016-08-01 2088 0.220871
2015-10-01 2083 0.220342
2016-07-01 2081 0.220130
2020-06-02 2066 0.218544
2013-01-02 2053 0.217168
2016-03-01 2029 0.214630
2012-10-01 2027 0.214418
2016-04-01 2026 0.214312
2012-02-01 2020 0.213678
2015-09-01 2014 0.213043
2011-12-01 2009 0.212514
2016-06-01 2003 0.211879
2013-02-01 1981 0.209552
2015-02-02 1980 0.209446
2015-03-02 1975 0.208918
2015-11-03 1971 0.208494
2015-04-01 1963 0.207648
2014-02-03 1962 0.207542
2016-05-02 1924 0.203523
2013-12-02 1909 0.201936
2015-07-01 1898 0.200772
2015-06-02 1880 0.198868
2013-03-01 1831 0.193685
2015-08-03 1819 0.192416
2012-03-01 1807 0.191146
2012-04-02 1805 0.190935
2013-10-02 1787 0.189031
2012-05-03 1787 0.189031
2012-11-02 1780 0.188290
2014-03-03 1775 0.187761
2020-11-04 1766 0.186809
2014-10-02 1761 0.186280
2012-09-03 1761 0.186280
2012-07-02 1753 0.185434
2015-05-04 1730 0.183001
2013-04-02 1709 0.180780
2013-09-02 1666 0.176231
2014-09-03 1655 0.175068
2014-04-02 1632 0.172635
2012-08-01 1632 0.172635
2014-05-05 1627 0.172106
2014-08-01 1623 0.171683
2014-07-02 1601 0.169355
2014-11-03 1599 0.169144
2020-10-02 1593 0.168509
2013-07-01 1581 0.167240
2010-12-02 1537 0.162585
2011-01-03 1536 0.162480
2011-03-01 1530 0.161845
2013-05-02 1519 0.160681
2012-06-04 1518 0.160576
2011-11-02 1480 0.156556
2014-06-05 1471 0.155604
2020-12-02 1437 0.152007
2011-10-04 1432 0.151478
2011-09-02 1428 0.151055
2011-02-01 1422 0.150421
2013-11-04 1418 0.149998
2011-07-01 1386 0.146613
2011-06-01 1368 0.144708
2013-08-02 1362 0.144074
2011-04-01 1350 0.142804
2013-06-03 1342 0.141958
2011-05-02 1273 0.134659
2009-02-03 1268 0.134130
2009-01-02 1245 0.131697
2011-08-02 1238 0.130957
2010-10-04 1215 0.128524
2008-12-01 1205 0.127466
2008-10-02 1187 0.125562
2010-09-02 1180 0.124822
2009-12-02 1179 0.124716
2010-11-02 1173 0.124081
2008-09-01 1155 0.122177
2010-08-02 1097 0.116042
2010-07-01 1080 0.114244
2008-01-03 1066 0.112763
2010-05-03 1066 0.112763
2008-03-03 1042 0.110224
2010-06-02 1041 0.110118
2020-08-03 1034 0.109378
2010-04-01 1034 0.109378
2009-03-03 1031 0.109060
2010-02-01 1026 0.108531
2008-02-04 1026 0.108531
2010-03-01 1024 0.108320
2009-04-02 1023 0.108214
2010-01-04 1022 0.108108
2009-10-02 1021 0.108002
2007-12-02 982 0.103877
2009-11-02 967 0.102290
2008-11-03 945 0.099963
2008-07-04 945 0.099963
2009-09-02 938 0.099223
2008-05-02 926 0.097953
2009-05-04 895 0.094674
2008-04-04 894 0.094568
2008-06-02 866 0.091606
2007-04-02 863 0.091289
2009-06-04 846 0.089491
2009-07-02 842 0.089068
2007-10-04 840 0.088856
2007-11-02 836 0.088433
2007-07-04 806 0.085260
2007-09-03 804 0.085048
2007-01-04 772 0.081663
2007-03-02 768 0.081240
2009-08-03 760 0.080394
2008-08-08 749 0.079230
2007-02-05 716 0.075739
2007-05-04 702 0.074258
2007-08-02 698 0.073835
2007-06-05 646 0.068335
2002-05-01 643 0.068017
2006-10-02 627 0.066325
2006-02-03 601 0.063574
2006-01-05 597 0.063151
2006-12-02 571 0.060401
2006-09-04 541 0.057228
2005-12-03 532 0.056276
2006-06-02 521 0.055112
2006-03-03 521 0.055112
2006-04-03 520 0.055006
2006-07-03 518 0.054795
2006-11-03 518 0.054795
2006-05-04 507 0.053631
2006-08-02 494 0.052256
2005-10-03 448 0.047390
2005-04-04 441 0.046649
2005-09-02 439 0.046438
2005-03-04 428 0.045274
2005-08-02 423 0.044745
2005-02-04 418 0.044216
2005-11-03 406 0.042947
2005-05-04 396 0.041889
2005-01-04 390 0.041255
2005-06-03 385 0.040726
2004-09-03 380 0.040197
2005-07-04 357 0.037764
2004-02-01 355 0.037552
2004-03-01 328 0.034696
2004-01-01 322 0.034061
2004-07-01 321 0.033956
2004-10-06 315 0.033321
2004-04-01 307 0.032475
2003-01-01 286 0.030253
2004-11-04 286 0.030253
2003-02-01 277 0.029301
2004-12-05 273 0.028878
2000-10-01 267 0.028244
2004-05-01 263 0.027820
2002-10-01 254 0.026868
2001-10-01 250 0.026445
2001-02-01 242 0.025599
2003-04-01 236 0.024964
2004-06-01 236 0.024964
2003-12-01 235 0.024859
2002-01-01 233 0.024647
2003-10-01 215 0.022743
2002-09-01 207 0.021897
2003-07-01 206 0.021791
2003-09-01 206 0.021791
2004-08-01 205 0.021685
2003-03-01 203 0.021474
2001-07-01 200 0.021156
2003-11-01 199 0.021050
1996-10-01 194 0.020522
2001-04-01 187 0.019781
2003-06-01 187 0.019781
2002-08-01 186 0.019675
2001-01-01 185 0.019569
2003-05-01 182 0.019252
2001-03-01 178 0.018829
2003-08-01 176 0.018617
2001-12-01 171 0.018089
2002-12-01 170 0.017983
2002-11-01 168 0.017771
2002-02-01 165 0.017454
1997-10-01 162 0.017137
1997-01-01 162 0.017137
2000-12-01 157 0.016608
2000-04-05 155 0.016396
2000-07-01 154 0.016290
1998-02-01 154 0.016290
2002-04-01 153 0.016184
2001-09-01 152 0.016079
2001-11-01 151 0.015973
1997-02-01 148 0.015656
1995-10-02 146 0.015444
1999-02-01 146 0.015444
2000-02-01 145 0.015338
2002-03-01 145 0.015338
1997-04-01 143 0.015127
1996-04-01 143 0.015127
1999-01-01 136 0.014386
1996-07-01 133 0.014069
2000-09-01 131 0.013857
1996-02-01 130 0.013752
2001-06-01 130 0.013752
1999-07-01 129 0.013646
1997-07-01 129 0.013646
2000-05-01 124 0.013117
1999-04-01 123 0.013011
1998-01-01 122 0.012905
2001-08-01 121 0.012800
1998-10-01 120 0.012694
1998-04-01 120 0.012694
2000-08-01 118 0.012482
2001-05-01 116 0.012271
2000-11-01 114 0.012059
2000-06-01 109 0.011530
2017-07-01 108 0.011424
1998-03-01 108 0.011424
1996-01-02 107 0.011319
2000-01-13 107 0.011319
1995-02-01 107 0.011319
1999-10-01 107 0.011319
2002-07-10 104 0.011001
2000-03-09 100 0.010578
1999-12-01 99 0.010472
1998-07-01 97 0.010261
1995-07-01 97 0.010261
1995-04-01 91 0.009626
1999-03-01 90 0.009520
1998-12-01 89 0.009415
1997-03-01 85 0.008991
2002-07-13 83 0.008780
1995-01-02 83 0.008780
1999-06-01 82 0.008674
1999-09-01 75 0.007934
2017-01-01 73 0.007722
1996-03-01 73 0.007722
2017-12-01 71 0.007510
1999-08-01 70 0.007405
1996-12-01 68 0.007193
1999-11-01 68 0.007193
2015-12-01 67 0.007087
2002-06-13 66 0.006982
2017-02-01 66 0.006982
2018-01-01 66 0.006982
2015-02-01 65 0.006876
2017-05-01 64 0.006770
2017-03-01 63 0.006664
2015-05-01 63 0.006664
2017-11-01 61 0.006453
2016-05-01 61 0.006453
2015-08-01 61 0.006453
1995-11-01 59 0.006241
1997-06-01 59 0.006241
2015-06-01 59 0.006241
1998-09-01 58 0.006135
1995-03-01 57 0.006030
2016-10-01 57 0.006030
2015-11-01 56 0.005924
1998-06-01 55 0.005818
2015-03-01 55 0.005818
1996-09-02 55 0.005818
1996-11-01 54 0.005712
2016-01-01 53 0.005606
2017-04-01 52 0.005501
2015-01-01 52 0.005501
1994-10-06 52 0.005501
2014-03-01 52 0.005501
1995-06-01 52 0.005501
1999-05-01 52 0.005501
2018-10-01 51 0.005395
2014-12-01 50 0.005289
2017-10-01 50 0.005289
2016-11-01 50 0.005289
1998-08-01 50 0.005289
2014-09-01 50 0.005289
2013-12-01 49 0.005183
2018-04-01 48 0.005077
2013-09-01 48 0.005077
2018-07-01 47 0.004972
1998-11-01 47 0.004972
1995-12-01 47 0.004972
2014-05-01 46 0.004866
1997-09-01 46 0.004866
2012-05-01 45 0.004760
1994-10-01 45 0.004760
1997-11-01 45 0.004760
2009-02-01 45 0.004760
2012-04-01 44 0.004654
2018-12-01 44 0.004654
1997-08-01 43 0.004549
2019-12-01 42 0.004443
1996-06-03 42 0.004443
2018-11-01 41 0.004337
1997-05-01 40 0.004231
1997-12-01 40 0.004231
1994-07-04 39 0.004125
2013-11-01 39 0.004125
1996-05-01 38 0.004020
2012-07-01 38 0.004020
1995-09-07 38 0.004020
1998-05-01 37 0.003914
2020-02-01 37 0.003914
1994-11-01 37 0.003914
2014-10-01 37 0.003914
1994-02-01 36 0.003808
2014-07-01 35 0.003702
2012-09-01 35 0.003702
2014-11-01 35 0.003702
2008-10-01 35 0.003702
1996-08-02 34 0.003597
1994-12-04 34 0.003597
2014-04-01 34 0.003597
2013-10-01 33 0.003491
2014-06-01 33 0.003491
2011-05-01 33 0.003491
1994-03-28 32 0.003385
2010-12-01 31 0.003279
2020-03-01 31 0.003279
2019-05-01 31 0.003279
2020-01-01 31 0.003279
2009-03-01 30 0.003173
2019-09-01 30 0.003173
1994-01-07 30 0.003173
2013-05-01 30 0.003173
2014-02-01 30 0.003173
2018-05-01 29 0.003068
2002-06-05 29 0.003068
2013-01-01 28 0.002962
2018-09-01 28 0.002962
2019-11-01 28 0.002962
2012-01-01 28 0.002962
1995-05-02 27 0.002856
2009-04-01 27 0.002856
2019-01-01 27 0.002856
2011-01-01 27 0.002856
2020-04-01 27 0.002856
2012-11-01 27 0.002856
1993-07-01 27 0.002856
1993-04-05 26 0.002750
2009-09-01 26 0.002750
2002-06-08 26 0.002750
2009-08-01 26 0.002750
2009-01-01 26 0.002750
2011-11-01 25 0.002645
2012-12-01 25 0.002645
2010-05-01 25 0.002645
2011-10-01 25 0.002645
2011-08-01 25 0.002645
1993-10-04 24 0.002539
2013-04-01 24 0.002539
2008-06-01 24 0.002539
2008-04-01 24 0.002539
2019-10-01 23 0.002433
2013-06-01 23 0.002433
2008-02-01 23 0.002433
2020-06-01 22 0.002327
2020-05-01 22 0.002327
1993-07-05 22 0.002327
1995-08-01 22 0.002327
2010-11-01 21 0.002221
2019-06-01 20 0.002116
2008-07-01 20 0.002116
2009-10-01 20 0.002116
2014-01-01 20 0.002116
2011-09-01 20 0.002116
2008-11-01 20 0.002116
2012-06-01 20 0.002116
2008-03-01 19 0.002010
2007-11-01 19 0.002010
2013-08-01 19 0.002010
2005-12-01 18 0.001904
2010-06-01 18 0.001904
1992-06-01 18 0.001904
2009-06-01 18 0.001904
1995-10-01 18 0.001904
2010-01-01 17 0.001798
2010-09-01 17 0.001798
2021-02-05 17 0.001798
2009-05-01 17 0.001798
2007-08-01 17 0.001798
1994-02-07 17 0.001798
2008-08-01 17 0.001798
2010-08-01 17 0.001798
1992-11-01 17 0.001798
2006-04-01 16 0.001692
1993-03-01 16 0.001692
2009-12-01 16 0.001692
1993-11-08 16 0.001692
1994-09-01 16 0.001692
1994-07-01 15 0.001587
1994-03-04 15 0.001587
2008-05-01 15 0.001587
2006-11-01 15 0.001587
2006-07-01 15 0.001587
1993-01-07 14 0.001481
1992-12-01 14 0.001481
1993-01-01 14 0.001481
1995-01-01 14 0.001481
2000-01-01 14 0.001481
2007-10-01 13 0.001375
2007-03-01 13 0.001375
2005-08-01 13 0.001375
2008-01-01 13 0.001375
1993-05-01 13 0.001375
1996-06-01 13 0.001375
1994-03-01 13 0.001375
2009-07-01 13 0.001375
2006-10-01 12 0.001269
1996-01-01 12 0.001269
2006-05-01 12 0.001269
2000-03-01 12 0.001269
2007-04-01 11 0.001164
2006-09-01 11 0.001164
2010-10-01 11 0.001164
2006-06-01 11 0.001164
1993-08-05 10 0.001058
2007-09-01 10 0.001058
2007-06-01 10 0.001058
2007-01-01 10 0.001058
2009-11-01 10 0.001058
2007-12-01 10 0.001058
2005-09-01 9 0.000952
1993-11-01 9 0.000952
1994-01-01 9 0.000952
2005-11-01 9 0.000952
2023-06-01 9 0.000952
1994-06-06 9 0.000952
2007-02-01 8 0.000846
1992-07-01 8 0.000846
2022-12-01 8 0.000846
1993-12-07 8 0.000846
2020-08-01 8 0.000846
1994-08-01 8 0.000846
2023-07-03 8 0.000846
1993-02-17 8 0.000846
1992-04-02 7 0.000740
1994-05-09 7 0.000740
1993-09-16 7 0.000740
2007-07-01 7 0.000740
1992-10-14 7 0.000740
2007-05-01 7 0.000740
1992-12-14 6 0.000635
1993-12-01 6 0.000635
2006-12-01 6 0.000635
1992-01-02 6 0.000635
2006-08-01 6 0.000635
2005-10-01 6 0.000635
2022-11-02 6 0.000635
1995-05-01 5 0.000529
1991-01-20 5 0.000529
1996-08-01 5 0.000529
1995-09-01 5 0.000529
1994-06-01 5 0.000529
1993-02-01 4 0.000423
2022-06-01 4 0.000423
1991-01-21 4 0.000423
2002-07-11 4 0.000423
2022-09-01 4 0.000423
1994-04-01 4 0.000423
1994-12-01 4 0.000423
1993-03-08 4 0.000423
1991-12-01 4 0.000423
1992-09-01 3 0.000317
1992-10-01 3 0.000317
2006-01-01 3 0.000317
1993-08-01 3 0.000317
1991-07-01 3 0.000317
1992-08-14 3 0.000317
1996-09-01 3 0.000317
2022-07-04 3 0.000317
1991-02-20 3 0.000317
1993-04-27 3 0.000317
1991-09-01 3 0.000317
2002-06-18 2 0.000212
1991-08-01 2 0.000212
2021-05-03 2 0.000212
2022-01-03 2 0.000212
2023-05-02 2 0.000212
1992-02-02 2 0.000212
2020-10-05 2 0.000212
2023-04-03 2 0.000212
2022-04-01 2 0.000212
2021-06-01 2 0.000212
1994-05-01 2 0.000212
1992-08-01 2 0.000212
2022-05-02 2 0.000212
2021-04-01 2 0.000212
1992-06-02 2 0.000212
1993-04-01 2 0.000212
1992-05-21 2 0.000212
1992-09-30 2 0.000212
1993-09-01 2 0.000212
1991-04-15 2 0.000212
1993-03-11 2 0.000212
1993-02-12 2 0.000212
1991-11-06 2 0.000212
1990-10-01 1 0.000106
1991-11-01 1 0.000106
1990-12-20 1 0.000106
1991-01-10 1 0.000106
1991-06-03 1 0.000106
1997-05-04 1 0.000106
1991-10-01 1 0.000106
1994-07-28 1 0.000106
1992-02-05 1 0.000106
1999-09-14 1 0.000106
1991-09-03 1 0.000106
1993-04-30 1 0.000106
1992-03-02 1 0.000106
1993-04-19 1 0.000106
1991-01-04 1 0.000106
1999-08-12 1 0.000106
1992-04-01 1 0.000106
2023-01-02 1 0.000106
2018-10-08 1 0.000106
1991-03-25 1 0.000106
1993-06-01 1 0.000106
1993-05-04 1 0.000106
1990-11-01 1 0.000106
2021-08-02 1 0.000106
1993-04-03 1 0.000106
2022-03-09 1 0.000106
2002-06-11 1 0.000106
2021-12-01 1 0.000106
2021-07-01 1 0.000106
2022-08-01 1 0.000106
2021-04-20 1 0.000106
msf_datelastrecurringdonorquota__c: Fecha de la ultima donacion recurrente.
Se puede observar que vacios. En la tabla de donaciones recurrentes tambien hay una fecha de ultima donacion recurrente, por lo que esta no aporta informacion adicional.
Analsis de distribución por variables
-> msf_datelastdonation__c: Variable fecha
In [631]:
# Vamos a realizar analisis por cada variable
var = "msf_datelastdonation__c"
In [632]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datelastdonation__c es 726227. Lo que supone un 73.05636256820488%
El nº de vacios para la variable msf_datelastdonation__c es 0. Lo que supone un 0.0%
Out[632]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c']
In [633]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[633]:
# Tot % Tot
2020-07-01 6905 2.578061
2022-12-02 6763 2.525043
2023-03-02 5936 2.216273
2020-06-01 3948 1.474031
2021-12-02 3672 1.370983
... ... ...
2010-05-16 1 0.000373
1993-01-29 1 0.000373
2011-01-25 1 0.000373
2008-08-29 1 0.000373
2009-11-15 1 0.000373

8428 rows × 2 columns

msf_datelastdonation__c: Fecha de la ultima donacion.
Se puede observar que .... vacios. Al igual que no se tiene en cuenta la de la primera, tampoco la de la ultima.
Analsis de distribución por variables
-> npsp__largest_soft_credit_date__c: Variable fecha
In [634]:
# Vamos a realizar analisis por cada variable
var = "npsp__largest_soft_credit_date__c"
In [635]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__largest_soft_credit_date__c es 994064. Lo que supone un 100.0%
El nº de vacios para la variable npsp__largest_soft_credit_date__c es 0. Lo que supone un 0.0%
Out[635]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c']
In [636]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[636]:
# Tot % Tot
npsp__largest_soft_credit_date__c: Fecha de la aportacion indirecta más importante.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npsp__first_soft_credit_date__c: Variable fecha
In [637]:
# Vamos a realizar analisis por cada variable
var = "npsp__first_soft_credit_date__c"
In [638]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__first_soft_credit_date__c es 994064. Lo que supone un 100.0%
El nº de vacios para la variable npsp__first_soft_credit_date__c es 0. Lo que supone un 0.0%
Out[638]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c']
In [639]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[639]:
# Tot % Tot
npsp__first_soft_credit_date__c: Fecha de la primera aportación indirecta.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_entrydatecurrentrecurringdonor__c: Variable fecha
In [640]:
# Vamos a realizar analisis por cada variable
var = "msf_entrydatecurrentrecurringdonor__c"
In [641]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_entrydatecurrentrecurringdonor__c es 414. Lo que supone un 0.041647217885367536%
El nº de vacios para la variable msf_entrydatecurrentrecurringdonor__c es 0. Lo que supone un 0.0%
In [642]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[642]:
# Tot % Tot
2000-02-01 3949 0.397424
2004-01-01 3842 0.386655
1994-10-01 3293 0.331404
2000-01-01 3274 0.329492
1995-02-01 2918 0.293665
... ... ...
2002-01-30 1 0.000101
2003-11-17 1 0.000101
2005-08-27 1 0.000101
2002-01-16 1 0.000101
2011-06-25 1 0.000101

7860 rows × 2 columns

msf_entrydatecurrentrecurringdonor__c: Fecha de la ultima entrada de socio.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npsp__last_soft_credit_date__c: Variable fecha
In [643]:
# Vamos a realizar analisis por cada variable
var = "npsp__last_soft_credit_date__c"
In [644]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__last_soft_credit_date__c es 994064. Lo que supone un 100.0%
El nº de vacios para la variable npsp__last_soft_credit_date__c es 0. Lo que supone un 0.0%
Out[644]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c']
In [645]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[645]:
# Tot % Tot
npsp__last_soft_credit_date__c: Fecha de la ultima aportación indirecta.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_firstentrydaterecurringdonor__c: Variable fecha
In [646]:
# Vamos a realizar analisis por cada variable
var = "msf_firstentrydaterecurringdonor__c"
In [647]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstentrydaterecurringdonor__c es 623. Lo que supone un 0.06267202111735261%
El nº de vacios para la variable msf_firstentrydaterecurringdonor__c es 0. Lo que supone un 0.0%
In [648]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[648]:
# Tot % Tot
2004-01-01 4974 0.500684
2000-02-01 4594 0.462433
1994-10-01 3823 0.384824
2000-01-01 3804 0.382912
1995-02-01 3374 0.339628
... ... ...
2003-02-04 1 0.000101
2003-07-11 1 0.000101
2004-08-12 1 0.000101
2003-01-07 1 0.000101
2010-04-24 1 0.000101

7926 rows × 2 columns

msf_firstentrydaterecurringdonor__c: Fecha de la primera entrada de socio.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npo02__firstclosedate__c: Variable fecha
In [649]:
# Vamos a realizar analisis por cada variable
var = "npo02__firstclosedate__c"
In [650]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__firstclosedate__c es 57038. Lo que supone un 5.73785993658356%
El nº de vacios para la variable npo02__firstclosedate__c es 0. Lo que supone un 0.0%
In [651]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[651]:
# Tot % Tot
2015-12-02 50662 5.406680
2016-12-01 50659 5.406360
2014-12-02 46591 4.972221
2017-12-04 45835 4.891540
2013-12-02 30543 3.259568
... ... ...
1999-02-15 1 0.000107
2009-02-21 1 0.000107
2001-06-21 1 0.000107
2016-09-04 1 0.000107
2012-03-05 1 0.000107

7986 rows × 2 columns

npo02__firstclosedate__c: Fecha de la primera donación de cualquier tipo.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_lastrecurringdonationdate__c: Variable fecha
In [652]:
# Vamos a realizar analisis por cada variable
var = "msf_lastrecurringdonationdate__c"
In [653]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lastrecurringdonationdate__c es 421682. Lo que supone un 42.42000515057381%
El nº de vacios para la variable msf_lastrecurringdonationdate__c es 0. Lo que supone un 0.0%
Out[653]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c']
In [654]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[654]:
# Tot % Tot
2020-03-12 2204 0.385058
2014-03-13 1942 0.339284
2023-05-10 1794 0.313427
2018-03-07 1616 0.282329
2018-04-09 1555 0.271672
... ... ...
2018-08-25 1 0.000175
2006-10-22 1 0.000175
1993-04-19 1 0.000175
2008-04-20 1 0.000175
2016-04-09 1 0.000175

7032 rows × 2 columns

msf_lastrecurringdonationdate__c: Fecha de la ultima baja de socio.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npo02__lastclosedate__c: Variable fecha
In [655]:
# Vamos a realizar analisis por cada variable
var = "npo02__lastclosedate__c"
In [656]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__lastclosedate__c es 994064. Lo que supone un 100.0%
El nº de vacios para la variable npo02__lastclosedate__c es 0. Lo que supone un 0.0%
Out[656]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c']
In [657]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[657]:
# Tot % Tot
npo02__lastclosedate__c: Fecha de la ultima donación de cualquier tipo.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> gender__c: Variable categorica
In [658]:
# Vamos a realizar analisis por cada variable
var = "gender__c"
In [659]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable gender__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable gender__c es 12555. Lo que supone un 1.2629971510888636%
In [660]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[660]:
# Tot % Tot
Female 548203 55.147656
Male 410016 41.246439
Other 23284 2.342304
12555 1.262997
H 5 0.000503
M 1 0.000101
gender__c: Genero.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_languagepreferer__c: Variable categorica
In [661]:
# Vamos a realizar analisis por cada variable
var = "msf_languagepreferer__c"
In [662]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_languagepreferer__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_languagepreferer__c es 0. Lo que supone un 0.0%
In [663]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[663]:
# Tot % Tot
ESP 858589 86.371602
CAT 119327 12.003955
GAL 10821 1.088562
EUS 5317 0.534875
ING 10 0.001006
msf_languagepreferer__c: lenguaje de comunicacion.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npo02__largestamount__c: Variable numerica
In [664]:
# Vamos a realizar analisis por cada variable
var = "npo02__largestamount__c"
In [665]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__largestamount__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__largestamount__c es 0. Lo que supone un 0.0%
In [666]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[666]:
# Tot % Tot
0.0 994064 100.0
npo02__largestamount__c: importe de la donacion más grande.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npo02__smallestamount__c: Variable numerica
In [667]:
# Vamos a realizar analisis por cada variable
var = "npo02__smallestamount__c"
In [668]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__smallestamount__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__smallestamount__c es 0. Lo que supone un 0.0%
In [669]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[669]:
# Tot % Tot
0.0 994064 100.0
npo02__smallestamount__c: importe de la donacion más pequeña.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npsp__first_soft_credit_amount__c: Variable numerica
In [670]:
# Vamos a realizar analisis por cada variable
var = "npsp__first_soft_credit_amount__c"
In [671]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__first_soft_credit_amount__c es 994064. Lo que supone un 100.0%
El nº de vacios para la variable npsp__first_soft_credit_amount__c es 0. Lo que supone un 0.0%
Out[671]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c']
In [672]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[672]:
# Tot % Tot
npsp__first_soft_credit_amount__c: importe de la primera aportacion indirecta.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npo02__lastoppamount__c: Variable numerica
In [673]:
# Vamos a realizar analisis por cada variable
var = "npo02__lastoppamount__c"
In [674]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__lastoppamount__c es 3428. Lo que supone un 0.3448470118624153%
El nº de vacios para la variable npo02__lastoppamount__c es 0. Lo que supone un 0.0%
In [675]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[675]:
# Tot % Tot
10.00 140562 14.189066
15.00 92234 9.310584
20.00 74631 7.533645
5.00 57681 5.822623
0.00 53610 5.411675
... ... ...
11250.00 1 0.000101
19.16 1 0.000101
169685.75 1 0.000101
120.08 1 0.000101
11.30 1 0.000101

1786 rows × 2 columns

npo02__lastoppamount__c: importe de la ultima aportacion.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npsp__last_soft_credit_amount__c: Variable numerica
In [676]:
# Vamos a realizar analisis por cada variable
var = "npsp__last_soft_credit_amount__c"
In [677]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__last_soft_credit_amount__c es 994064. Lo que supone un 100.0%
El nº de vacios para la variable npsp__last_soft_credit_amount__c es 0. Lo que supone un 0.0%
Out[677]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c']
In [678]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[678]:
# Tot % Tot
npsp__last_soft_credit_amount__c: importe de la ultima aportacion indirecta.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_annualizedquotachange__c: Variable numerica
In [679]:
# Vamos a realizar analisis por cada variable
var = "msf_annualizedquotachange__c"
In [680]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_annualizedquotachange__c es 444680. Lo que supone un 44.733538283249366%
El nº de vacios para la variable msf_annualizedquotachange__c es 0. Lo que supone un 0.0%
Out[680]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c']
In [681]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[681]:
# Tot % Tot
48.00 207919 37.845842
24.00 46376 8.441454
72.00 45925 8.359362
60.00 43108 7.846606
120.00 28119 5.118278
36.00 27732 5.047835
84.00 22833 4.156109
50.00 10626 1.934166
0.00 10586 1.926885
30.00 7533 1.371172
144.00 7394 1.345871
40.00 5521 1.004944
25.00 5257 0.956890
45.00 4661 0.848405
20.00 3932 0.715711
108.00 3853 0.701331
28.00 3495 0.636167
15.00 3448 0.627612
10.00 3315 0.603403
64.00 3000 0.546066
96.00 2767 0.503655
35.88 2766 0.503473
35.00 2551 0.464338
12.00 2207 0.401723
100.00 1840 0.334921
70.00 1759 0.320177
8.00 1751 0.318721
7.00 1666 0.303249
5.00 1659 0.301975
20.04 1625 0.295786
52.00 1619 0.294694
90.00 1468 0.267208
56.00 1443 0.262658
240.00 1280 0.232988
6.00 1260 0.229348
2.00 1217 0.221521
29.90 1182 0.215150
18.00 1048 0.190759
47.80 989 0.180020
80.00 966 0.175833
55.00 953 0.173467
14.95 857 0.155993
132.00 853 0.155265
16.00 850 0.154719
88.00 755 0.137427
44.00 714 0.129964
47.76 658 0.119771
180.00 613 0.111580
65.00 613 0.111580
168.00 610 0.111033
32.00 592 0.107757
76.00 574 0.104481
119.40 528 0.096108
14.00 505 0.091921
17.00 503 0.091557
22.00 490 0.089191
59.64 451 0.082092
33.00 448 0.081546
42.00 410 0.074629
54.00 395 0.071899
44.85 388 0.070625
140.00 371 0.067530
192.00 351 0.063890
71.60 334 0.060795
27.00 325 0.059157
156.00 302 0.054971
4.00 301 0.054789
200.00 299 0.054425
34.00 267 0.048600
160.00 263 0.047872
32.88 231 0.042047
8.97 199 0.036222
11.00 198 0.036040
41.00 189 0.034402
21.00 188 0.034220
68.00 188 0.034220
360.00 181 0.032946
9.00 169 0.030762
44.80 167 0.030398
110.00 143 0.026029
23.92 141 0.025665
130.00 136 0.024755
59.75 133 0.024209
31.00 129 0.023481
300.00 126 0.022935
142.80 124 0.022571
55.76 109 0.019840
480.00 107 0.019476
26.00 106 0.019294
58.00 96 0.017474
3.00 96 0.017474
51.96 94 0.017110
19.00 89 0.016200
62.00 86 0.015654
47.88 85 0.015472
38.00 82 0.014926
17.94 79 0.014380
99.40 79 0.014380
17.15 72 0.013106
40.08 70 0.012742
11.96 66 0.012013
75.00 64 0.011649
104.00 59 0.010739
46.00 59 0.010739
49.70 58 0.010557
52.60 55 0.010011
47.84 54 0.009829
105.00 48 0.008737
89.50 46 0.008373
83.52 44 0.008009
53.00 44 0.008009
400.00 40 0.007281
13.00 40 0.007281
66.00 39 0.007099
46.85 36 0.006553
47.00 35 0.006371
37.00 35 0.006371
600.00 33 0.006007
35.76 33 0.006007
5.98 32 0.005825
95.00 31 0.005643
32.04 30 0.005461
43.00 29 0.005279
720.00 28 0.005097
49.00 26 0.004733
2.99 25 0.004551
51.00 24 0.004369
150.00 24 0.004369
35.88 22 0.004004
119.00 22 0.004004
15.96 22 0.004004
66.96 21 0.003822
63.72 20 0.003640
125.00 20 0.003640
178.20 20 0.003640
119.28 20 0.003640
55.68 19 0.003458
78.00 19 0.003458
320.00 18 0.003276
1200.00 17 0.003094
27.92 17 0.003094
139.20 17 0.003094
92.00 16 0.002912
118.99 16 0.002912
85.00 15 0.002730
112.00 15 0.002730
63.64 14 0.002548
121.80 14 0.002548
51.72 14 0.002548
228.00 13 0.002366
26.91 12 0.002184
40.86 12 0.002184
59.00 12 0.002184
107.04 11 0.002002
280.00 11 0.002002
107.40 11 0.002002
14.16 11 0.002002
118.56 10 0.001820
57.00 10 0.001820
166.56 10 0.001820
69.60 9 0.001638
36.87 9 0.001638
51.82 9 0.001638
29.00 8 0.001456
124.00 8 0.001456
56.64 8 0.001456
46.56 8 0.001456
20.93 8 0.001456
39.00 8 0.001456
71.64 7 0.001274
23.00 7 0.001274
237.60 7 0.001274
95.16 7 0.001274
119.40 6 0.001092
28.31 6 0.001092
39.88 6 0.001092
216.00 6 0.001092
29.76 6 0.001092
45.60 6 0.001092
420.00 6 0.001092
204.00 6 0.001092
115.00 5 0.000910
276.00 5 0.000910
960.00 5 0.000910
47.52 5 0.000910
116.00 5 0.000910
357.00 5 0.000910
59.64 5 0.000910
51.80 5 0.000910
1440.00 5 0.000910
126.00 5 0.000910
89.49 5 0.000910
94.00 5 0.000910
26.32 5 0.000910
220.00 4 0.000728
238.00 4 0.000728
97.92 4 0.000728
8.97 4 0.000728
19.95 4 0.000728
74.00 4 0.000728
135.00 4 0.000728
51.84 4 0.000728
6.58 4 0.000728
79.50 4 0.000728
47.64 4 0.000728
1000.00 4 0.000728
114.00 4 0.000728
63.00 4 0.000728
21.93 4 0.000728
47.76 4 0.000728
59.65 4 0.000728
44.64 4 0.000728
41.88 4 0.000728
67.00 4 0.000728
68.04 3 0.000546
714.00 3 0.000546
21.60 3 0.000546
16.80 3 0.000546
14.88 3 0.000546
260.00 3 0.000546
2400.00 3 0.000546
34.90 3 0.000546
106.00 3 0.000546
33.89 3 0.000546
128.00 3 0.000546
800.00 3 0.000546
82.00 3 0.000546
136.00 3 0.000546
25.04 3 0.000546
49.85 3 0.000546
129.25 3 0.000546
16.95 3 0.000546
59.76 3 0.000546
148.00 3 0.000546
55.92 2 0.000364
53.82 2 0.000364
145.00 2 0.000364
3.99 2 0.000364
780.00 2 0.000364
31.90 2 0.000364
17.34 2 0.000364
3.34 2 0.000364
32.88 2 0.000364
60.60 2 0.000364
38.76 2 0.000364
162.00 2 0.000364
75.60 2 0.000364
118.80 2 0.000364
13.96 2 0.000364
500.00 2 0.000364
356.40 2 0.000364
6.68 2 0.000364
83.00 2 0.000364
64.08 2 0.000364
14400.00 2 0.000364
288.00 2 0.000364
20.95 2 0.000364
43.88 2 0.000364
44.04 2 0.000364
74.04 2 0.000364
102.00 2 0.000364
33.48 2 0.000364
324.00 2 0.000364
44.90 2 0.000364
24.12 2 0.000364
540.00 2 0.000364
122.40 1 0.000182
35.76 1 0.000182
28.80 1 0.000182
264.00 1 0.000182
100.08 1 0.000182
13.60 1 0.000182
115.20 1 0.000182
83.60 1 0.000182
840.00 1 0.000182
154.00 1 0.000182
5.50 1 0.000182
63.60 1 0.000182
141.12 1 0.000182
384.00 1 0.000182
580.00 1 0.000182
86.00 1 0.000182
33.90 1 0.000182
-720.00 1 0.000182
59.28 1 0.000182
-96.00 1 0.000182
143.76 1 0.000182
39.60 1 0.000182
43.84 1 0.000182
29.85 1 0.000182
97.00 1 0.000182
4.50 1 0.000182
202.20 1 0.000182
41.16 1 0.000182
440.00 1 0.000182
87.00 1 0.000182
34.99 1 0.000182
14.50 1 0.000182
475.20 1 0.000182
6.98 1 0.000182
81.52 1 0.000182
71.76 1 0.000182
51.96 1 0.000182
55.68 1 0.000182
44.25 1 0.000182
60.48 1 0.000182
51.77 1 0.000182
59.70 1 0.000182
29.95 1 0.000182
65.76 1 0.000182
3600.00 1 0.000182
81.12 1 0.000182
138.00 1 0.000182
51.85 1 0.000182
190.80 1 0.000182
107.40 1 0.000182
237.96 1 0.000182
560.00 1 0.000182
277.60 1 0.000182
8.66 1 0.000182
49.85 1 0.000182
1920.00 1 0.000182
49.90 1 0.000182
80.04 1 0.000182
296.97 1 0.000182
52.64 1 0.000182
135.44 1 0.000182
64.65 1 0.000182
27.88 1 0.000182
60.04 1 0.000182
41.86 1 0.000182
28.68 1 0.000182
17.95 1 0.000182
-192.00 1 0.000182
109.25 1 0.000182
52.78 1 0.000182
64.32 1 0.000182
900.00 1 0.000182
59.85 1 0.000182
-168.00 1 0.000182
43.86 1 0.000182
29.99 1 0.000182
713.88 1 0.000182
32.10 1 0.000182
59.80 1 0.000182
127.28 1 0.000182
131.88 1 0.000182
83.49 1 0.000182
57.36 1 0.000182
48.85 1 0.000182
47.83 1 0.000182
44.85 1 0.000182
179.00 1 0.000182
28.98 1 0.000182
24000.00 1 0.000182
139.00 1 0.000182
91.92 1 0.000182
178.80 1 0.000182
348.00 1 0.000182
16.08 1 0.000182
297.47 1 0.000182
166.68 1 0.000182
55.80 1 0.000182
77.00 1 0.000182
32.28 1 0.000182
67.76 1 0.000182
17.94 1 0.000182
-108.00 1 0.000182
28.08 1 0.000182
52.08 1 0.000182
33.04 1 0.000182
32.14 1 0.000182
165.12 1 0.000182
432.00 1 0.000182
250.00 1 0.000182
15.92 1 0.000182
98.56 1 0.000182
129.00 1 0.000182
63.24 1 0.000182
71.88 1 0.000182
50.68 1 0.000182
61.68 1 0.000182
103.44 1 0.000182
119.20 1 0.000182
5.60 1 0.000182
297.60 1 0.000182
122.00 1 0.000182
87.52 1 0.000182
20.40 1 0.000182
143.40 1 0.000182
158.60 1 0.000182
236.00 1 0.000182
81.00 1 0.000182
52.88 1 0.000182
16.44 1 0.000182
56.04 1 0.000182
52.68 1 0.000182
35.40 1 0.000182
24.60 1 0.000182
257.57 1 0.000182
660.00 1 0.000182
52.80 1 0.000182
1600.00 1 0.000182
372.00 1 0.000182
69.00 1 0.000182
640.00 1 0.000182
53.64 1 0.000182
44.40 1 0.000182
67.64 1 0.000182
38.28 1 0.000182
350.00 1 0.000182
43.08 1 0.000182
10.50 1 0.000182
700.00 1 0.000182
37.90 1 0.000182
19.80 1 0.000182
63.76 1 0.000182
61.00 1 0.000182
63.80 1 0.000182
109.92 1 0.000182
15.95 1 0.000182
127.28 1 0.000182
147.72 1 0.000182
19.94 1 0.000182
64.75 1 0.000182
133.36 1 0.000182
1320.00 1 0.000182
66.84 1 0.000182
40.68 1 0.000182
197.98 1 0.000182
89.00 1 0.000182
msf_annualizedquotachange__c: incremento de cuota anualizado que se le pediria.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_relationshiplevel__c: Variable categorica
In [682]:
# Vamos a realizar analisis por cada variable
var = "msf_relationshiplevel__c"
In [683]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_relationshiplevel__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_relationshiplevel__c es 4. Lo que supone un 0.00040238857860258495%
In [684]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[684]:
# Tot % Tot
a0l0O00000k727RQAQ 942828 94.845805
a0l0O00000k727QQAQ 27803 2.796902
a0l0O00000k727SQAQ 17898 1.800488
a0l0O00000k727TQAQ 5328 0.535982
a0l0O00000k727UQAQ 203 0.020421
4 0.000402
msf_relationshiplevel__c: tipo de relacion que se desea con el contacto.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_ltvcont__c: Variable numerica
In [685]:
# Vamos a realizar analisis por cada variable
var = "msf_ltvcont__c"
In [686]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_ltvcont__c es 57038. Lo que supone un 5.73785993658356%
El nº de vacios para la variable msf_ltvcont__c es 0. Lo que supone un 0.0%
In [687]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[687]:
# Tot % Tot
30.00 12590 1.343613
60.00 12133 1.294841
10.00 11459 1.222912
20.00 11239 1.199433
40.00 8630 0.920999
... ... ...
1769.28 1 0.000107
825.43 1 0.000107
3180.61 1 0.000107
7776.81 1 0.000107
1628.70 1 0.000107

83992 rows × 2 columns

msf_ltvcont__c: valor de todas las aportaciones.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> mailingstate: Variable categorica
In [688]:
# Vamos a realizar analisis por cada variable
var = "mailingstate"
In [689]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable mailingstate es 0. Lo que supone un 0.0%
El nº de vacios para la variable mailingstate es 53511. Lo que supone un 5.383053807400731%
In [690]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[690]:
# Tot % Tot
MADRID 161171 16.213342
BARCELONA 120407 12.112600
53511 5.383054
VALENCIA/VALÈNCIA 50993 5.129750
BIZKAIA 38416 3.864540
SEVILLA 29253 2.942768
MÁLAGA 29229 2.940354
ALICANTE/ALACANT 28276 2.844485
A CORUÑA 26069 2.622467
GIPUZKOA 25125 2.527503
ILLES BALEARS 23165 2.330333
PONTEVEDRA 20583 2.070591
MURCIA 19573 1.968988
ASTURIAS 17610 1.771516
SANTA CRUZ DE TENERIFE 17540 1.764474
CÁDIZ 16967 1.706832
LAS PALMAS 16013 1.610862
GRANADA 13940 1.402324
Madrid 13426 1.350617
ZARAGOZA 13156 1.323456
GIRONA 11384 1.145198
NAVARRA 10910 1.097515
Barcelona 10571 1.063412
CANTABRIA 10477 1.053956
TARRAGONA 9630 0.968751
VALLADOLID 9453 0.950945
ARABA/ÁLAVA 9312 0.936761
CASTELLÓN/CASTELLÓ 8882 0.893504
CÓRDOBA 7210 0.725305
TOLEDO 6862 0.690298
LEÓN 6798 0.683859
HUELVA 6142 0.617868
BURGOS 5521 0.555397
BADAJOZ 5436 0.546846
LLEIDA 5309 0.534070
Vizcaya 5246 0.527733
CIUDAD REAL 5102 0.513247
Alicante/Alacant 5095 0.512542
ALMERÍA 4785 0.481357
JAÉN 4694 0.472203
LA RIOJA 4550 0.457717
GUADALAJARA 4354 0.438000
Murcia 4282 0.430757
Sevilla 4129 0.415366
SALAMANCA 3971 0.399471
CÁCERES 3951 0.397459
OURENSE 3628 0.364966
ALBACETE 3616 0.363759
LUGO 3578 0.359937
Malaga 3569 0.359031
Granada 3556 0.357723
Valencia/Valencia 3480 0.350078
Cadiz 3064 0.308230
HUESCA 2838 0.285495
A Coruña 2710 0.272618
Valencia 2437 0.245155
SEGOVIA 2280 0.229361
Santa Cruz de Tenerife 2164 0.217692
PALENCIA 2015 0.202703
CUENCA 1942 0.195360
ÁVILA 1926 0.193750
ZAMORA 1875 0.188620
Alicante 1617 0.162666
Guipuzcoa 1582 0.159145
SORIA 1423 0.143150
Málaga 1378 0.138623
Badajoz 1316 0.132386
TERUEL 1272 0.127960
Salamanca 1258 0.126551
Pontevedra 1048 0.105426
Cádiz 1033 0.103917
VALENCIA 956 0.096171
Asturias 941 0.094662
Almeria 903 0.090839
Illes Balears 899 0.090437
Ciudad Real 845 0.085005
Las Palmas 841 0.084602
Navarra 824 0.082892
Cantabria 747 0.075146
Bizkaia 736 0.074039
MELILLA 720 0.072430
MALAGA 707 0.071122
Huelva 689 0.069311
Tarragona 687 0.069110
CEUTA 674 0.067802
Zaragoza 673 0.067702
Girona 668 0.067199
Valladolid 536 0.053920
ALICANTE 535 0.053819
Toledo 494 0.049695
Santa Cruz De Tenerife 488 0.049091
Valencia/València 457 0.045973
Almería 442 0.044464
Caceres 429 0.043156
Valencia/Valéncia 421 0.042351
Baleares 418 0.042050
Gipuzkoa 406 0.040842
Guipúzcoa 406 0.040842
Burgos 402 0.040440
Castellon/Castello 360 0.036215
VIZCAYA 343 0.034505
Cordoba 327 0.032895
CADIZ 319 0.032090
Guadalajara 298 0.029978
Lleida 297 0.029877
Albacete 295 0.029676
La Rioja 294 0.029576
Jaen 291 0.029274
Lugo 284 0.028570
Cáceres 242 0.024345
Ourense 221 0.022232
alava 219 0.022031
Córdoba 214 0.021528
León 207 0.020824
Huesca 201 0.020220
Leon 195 0.019616
Castellon 190 0.019113
Segovia 189 0.019013
Jaén 179 0.018007
Zamora 175 0.017605
Castellón 168 0.016900
GUIPUZCOA 155 0.015593
Alava 150 0.015090
CASTELLON 148 0.014888
LEON 143 0.014385
Alacant 142 0.014285
MAlaga 133 0.013379
CORDOBA 133 0.013379
CAdiz 130 0.013078
Álava 129 0.012977
València 124 0.012474
Castellón/Castelló 122 0.012273
Melilla 122 0.012273
Cuenca 122 0.012273
ALMERIA 114 0.011468
Valencia/ValEncia 113 0.011367
JAEN 109 0.010965
Palencia 105 0.010563
Araba/Alava 91 0.009154
ALAVA 82 0.008249
Teruel 81 0.008148
Tenerife 80 0.008048
CACERES 75 0.007545
TENERIFE 72 0.007243
Soria 71 0.007142
Ávila 64 0.006438
GuipUzcoa 63 0.006338
BALEARES 61 0.006136
Ceuta 61 0.006136
madrid 59 0.005935
VALENCIA/VALéNCIA 53 0.005332
ISLAS BALEARES 51 0.005130
BILBAO 47 0.004728
VALENCIA/VALÉNCIA 47 0.004728
AVILA 43 0.004326
Guipuzkoa 40 0.004024
avila 38 0.003823
MALLORCA 37 0.003722
LAS PALMAS DE GRAN CANARIA 36 0.003621
CORUÑA 34 0.003420
Islas Baleares 33 0.003320
Avila 33 0.003320
GERONA 32 0.003219
CANARIAS 32 0.003219
GRAN CANARIA 30 0.003018
CAceres 28 0.002817
LA CORUÑA 27 0.002716
ORENSE 25 0.002515
A Coru?a 25 0.002515
Bilbao 24 0.002414
Valencia/Valéncia 23 0.002314
barcelona 23 0.002314
CastellOn/CastellO 22 0.002213
VIGO 21 0.002113
PALMA DE MALLORCA 19 0.001911
MaLAGA 19 0.001911
CaDIZ 18 0.001811
malaga 18 0.001811
valencia 17 0.001710
GALICIA 17 0.001710
AlmerIa 17 0.001710
Castelló 16 0.001610
Málaga 16 0.001610
Bizcaia 16 0.001610
alicante 15 0.001509
LERIDA 15 0.001509
PAMPLONA 14 0.001408
IBIZA 14 0.001408
sevilla 13 0.001308
M?laga 13 0.001308
GUIPUZCUA 13 0.001308
Guipuzcua 13 0.001308
GUIPUZKOA 13 0.001308
SevillA 13 0.001308
ARABA/ALAVA 12 0.001207
ALACANT 12 0.001207
CASTELLÓN 12 0.001207
OVIEDO 12 0.001207
Las Palmas de Gran Canarias 11 0.001107
MadrId 11 0.001107
LANZAROTE 11 0.001107
VIZCAIA 10 0.001006
JaEn 10 0.001006
SANTA CRUZ TENERIFE 10 0.001006
LAS PALMAS DE GRAN CANARIAS 10 0.001006
COrdoba 10 0.001006
GUIPÚZCOA 9 0.000905
ARABA 9 0.000905
cadiz 9 0.000905
CáDIZ 9 0.000905
SANTANDER 9 0.000905
Guipúzcoa 9 0.000905
Gerona 9 0.000905
Araba/Álava 9 0.000905
Orense 8 0.000805
badajoz 8 0.000805
AlIcante/Alacant 8 0.000805
BArcelonA 8 0.000805
asturias 8 0.000805
Santander 8 0.000805
Vizkaya 8 0.000805
Bizcaya 8 0.000805
murcia 8 0.000805
ILLES BALEARES 8 0.000805
SAN SEBASTIAN 8 0.000805
salamanca 7 0.000704
VIZKAYA 7 0.000704
GIJON 7 0.000704
Coruña 7 0.000704
LeOn 7 0.000704
ÁLAVA 7 0.000704
A coruña 7 0.000704
CASTELLON/CASTELLO 7 0.000704
La Coruña 7 0.000704
CORUÑA,A 7 0.000704
MENORCA 7 0.000704
MurcIa 7 0.000704
toledo 7 0.000704
MAlAgA 7 0.000704
Mallorca 7 0.000704
Las Palmas De Gran Canaria 6 0.000604
ValencIa 6 0.000604
Araba 6 0.000604
A CORU?A 6 0.000604
pontevedra 6 0.000604
A Coruña 6 0.000604
Santa Cruz de TenerIfe 6 0.000604
vizcaya 6 0.000604
BIZCAIA 6 0.000604
Gran Canaria 6 0.000604
Las Palmas de Gran Canaria 6 0.000604
C?diz 6 0.000604
a coruña 6 0.000604
Vigo 5 0.000503
VITORIA 5 0.000503
CORUÑA, A 5 0.000503
LOGROÑO 5 0.000503
CASTELLON DE LA PLANA 5 0.000503
Illes Baleares 5 0.000503
MAdrid 5 0.000503
Cádiz 5 0.000503
Valencia/valència 5 0.000503
PAIS VASCO 5 0.000503
valladolid 5 0.000503
Cartagena 5 0.000503
Gipuzcoa 5 0.000503
Almer?a 5 0.000503
LA CORUNA 5 0.000503
ANDORRA 5 0.000503
BIZCAYA 4 0.000402
Alicante/alacant 4 0.000402
ValencIa/ValencIa 4 0.000402
santa cruz de tenerife 4 0.000402
CaCERES 4 0.000402
ISLAS CANARIAS 4 0.000402
CORU?A 4 0.000402
Logroño 4 0.000402
FRANCIA 4 0.000402
Palma De Mallorca 4 0.000402
SevIlla 4 0.000402
Guip?zcoa 4 0.000402
Guipuzkua 4 0.000402
C?ceres 4 0.000402
Oviedo 4 0.000402
CáCERES 4 0.000402
VAlenciA/VAlenciA 4 0.000402
Canarias 4 0.000402
LEoN 4 0.000402
Le?n 4 0.000402
Castell?n 4 0.000402
CARTAGENA 4 0.000402
GUIPUZKUA 4 0.000402
illes balears 4 0.000402
segovia 4 0.000402
GuIpuzcoa 4 0.000402
Lanzarote 4 0.000402
PALMA 4 0.000402
Pamplona 4 0.000402
CastellOn 3 0.000302
caceres 3 0.000302
VizcAyA 3 0.000302
DONOSTIA 3 0.000302
Asturia 3 0.000302
CASTILLA Y LEON 3 0.000302
jaen 3 0.000302
C?rdoba 3 0.000302
girona 3 0.000302
CASTELLoN/CASTELLo 3 0.000302
Hessen 3 0.000302
SANTIAGO DE COMPOSTELA 3 0.000302
Alacant / Alicante 3 0.000302
Malága 3 0.000302
cantabria 3 0.000302
MáLAGA 3 0.000302
ALEMANIA 3 0.000302
JAeN 3 0.000302
cordoba 3 0.000302
STA. CRUZ DE TENERIFE 3 0.000302
bizkaia 3 0.000302
CIUDAD 3 0.000302
Albecete 3 0.000302
DONOSTI 3 0.000302
ALABA 3 0.000302
CoRDOBA 3 0.000302
lugo 3 0.000302
VALENCIANA 3 0.000302
AlicAnte/AlAcAnt 3 0.000302
LA PALMA 3 0.000302
Santa Cruz Tenerife 3 0.000302
CIudad Real 3 0.000302
?lava 3 0.000302
Las Palmas De Gran Canarias 3 0.000302
burgos 3 0.000302
ZAGAROZA 2 0.000201
POTEVEDRA 2 0.000201
Fuerteventura 2 0.000201
Guipizcoa 2 0.000201
VIzcaya 2 0.000201
LA PALMAS DE GRAN CANARIA 2 0.000201
PALMA MALLORCA 2 0.000201
Cáceres 2 0.000201
Gijón 2 0.000201
CadIz 2 0.000201
Guizpuzcoa 2 0.000201
Balears 2 0.000201
Marbella 2 0.000201
GRAN CANARIAS 2 0.000201
LORCA 2 0.000201
RIOJA,LA 2 0.000201
BRETAÑA 2 0.000201
EXTREMADURA 2 0.000201
BALEARES, ISLAS 2 0.000201
Sud-Kivu 2 0.000201
ESPAÑA 2 0.000201
SAN CRUZ DE TENERIFE 2 0.000201
tenerife 2 0.000201
almeria 2 0.000201
Alicante/Alacantt 2 0.000201
GUIPUZ 2 0.000201
Bruxelles 2 0.000201
Zaragoz 2 0.000201
SEVILA 2 0.000201
STA CRUZ DE TENERIFE 2 0.000201
mallorca 2 0.000201
BALEARS 2 0.000201
CASTELLÓ 2 0.000201
Palma de Mallorca 2 0.000201
MARBELLA 2 0.000201
VALLLADOLID 2 0.000201
ASTURIA 2 0.000201
navarra 2 0.000201
BAJADOZ 2 0.000201
Portugal 2 0.000201
Valencai 2 0.000201
aLAVA 2 0.000201
zamora 2 0.000201
tarragona 2 0.000201
GRANADILLA DE ABONA 2 0.000201
Vizkaia 2 0.000201
bilbao 2 0.000201
València/Valencia 2 0.000201
Galicia 2 0.000201
VITORIA-GASTEIZ 2 0.000201
Elche 2 0.000201
GrAnAdA 2 0.000201
Gipozkoa 2 0.000201
Vitoria 2 0.000201
SALAMNCA 2 0.000201
las palmas 2 0.000201
Valladolidad 2 0.000201
EXTRANJERO 2 0.000201
ACORUÑA 2 0.000201
Castellon De La Plana 2 0.000201
zaragoza 2 0.000201
Islas Canarias 2 0.000201
CANARIA 2 0.000201
TARRRAGONA 2 0.000201
SALMANCA 2 0.000201
SANTA CRUZ 2 0.000201
granada 2 0.000201
Illes Balers 2 0.000201
LLeida 2 0.000201
Asturies 2 0.000201
GIPUZCOA 2 0.000201
Zaragona 2 0.000201
ARABA/aLAVA 2 0.000201
Santa cruz de Tenerife 2 0.000201
Ja?n 1 0.000101
VALENCIA/VALENCIA 1 0.000101
Bizakia 1 0.000101
Alemania 1 0.000101
Kadiogo 1 0.000101
Seilla 1 0.000101
SANTANDER(CANTABRIA) 1 0.000101
ABACETE 1 0.000101
ciudad real 1 0.000101
Murcio 1 0.000101
VICAYA 1 0.000101
LA RiojA 1 0.000101
A CoruñA 1 0.000101
Araba/alava 1 0.000101
Gelderland 1 0.000101
VILLA NUEVA DE FRESNO 1 0.000101
ILLESBALEARS 1 0.000101
Servilla 1 0.000101
A CORUÑA 1 0.000101
SANTACRUZ DE TENERIFE 1 0.000101
Roma 1 0.000101
MALLORCA-BALEARES- 1 0.000101
NO RESIDENTE 1 0.000101
TERRUEL 1 0.000101
TERRAGONA 1 0.000101
Illes Ballears 1 0.000101
Vallodolid 1 0.000101
Peñiscola 1 0.000101
MALGA 1 0.000101
guipuzkoa 1 0.000101
GUIPOUZCOA 1 0.000101
palencia 1 0.000101
PONTEVEDRA. SALVATERRA DE MIÑO 1 0.000101
BIZKAIYA 1 0.000101
Pontevdra 1 0.000101
PONTEVDRA 1 0.000101
Mállaga 1 0.000101
Cundinamarca 1 0.000101
Addis Ababa 1 0.000101
NAVARA 1 0.000101
SANTA CRUZ DETENERIFE 1 0.000101
OTUR VALDES (LUARCA) 1 0.000101
Gipizkoa 1 0.000101
LLIEDA 1 0.000101
GIPUSCUA 1 0.000101
A CORNUÑA 1 0.000101
santarder 1 0.000101
Illes De Balears 1 0.000101
Guadalaja 1 0.000101
SEVIILA 1 0.000101
balears 1 0.000101
Donosti 1 0.000101
Santiago 1 0.000101
ALBAZETE 1 0.000101
SANTA CRUZ DE TRENERIFE 1 0.000101
Extremadura 1 0.000101
VALENCIO 1 0.000101
TALABERA DE LA REINA 1 0.000101
LAS PALAMAS DE GRAN CANARIA 1 0.000101
GORLIZ 1 0.000101
ALICANTE / DILAJOYOSA 1 0.000101
HAMBURG 1 0.000101
Donostia 1 0.000101
LA CORU?A 1 0.000101
BARACALDO 1 0.000101
Getxo/Bizkaia 1 0.000101
Santa Cruz De La Palma 1 0.000101
g.c 1 0.000101
GELVES 1 0.000101
la coruña 1 0.000101
Águilas 1 0.000101
MARIA DE HUERVA 1 0.000101
Araba/Álaba 1 0.000101
PO 1 0.000101
DE JAEN 1 0.000101
OURENSE/ORENSE 1 0.000101
Ibiza 1 0.000101
Tarrronga 1 0.000101
Alicante (Alacant) 1 0.000101
VIZCAYIA 1 0.000101
Barcleona 1 0.000101
Antwerp 1 0.000101
Guipuscoa 1 0.000101
Valdegovía 1 0.000101
Guadajalara 1 0.000101
IRUN 1 0.000101
CARTEGENA 1 0.000101
CASTELLoN 1 0.000101
ciudda real 1 0.000101
PONTEVEDRO 1 0.000101
LAS PALMAS LANZAROTE 1 0.000101
Pontebra 1 0.000101
Bizkaya 1 0.000101
Las Palamas 1 0.000101
Alicanta 1 0.000101
Cantábria 1 0.000101
Paraná 1 0.000101
CARRERA DEL CARMEN 22 1 0.000101
Pontevendra 1 0.000101
Taragona 1 0.000101
teruel 1 0.000101
VALLADOLD 1 0.000101
CASTILLA 1 0.000101
Gipuzkia 1 0.000101
PALMAS DE GRAN CANARIAS 1 0.000101
guipuzcoa 1 0.000101
NULL 1 0.000101
Arava/Álava 1 0.000101
AvilA 1 0.000101
SANTIAGO COMPOSTELA 1 0.000101
gijón 1 0.000101
CORUÑA A 1 0.000101
Araba/álava 1 0.000101
BÉLGICA 1 0.000101
Gipuzloa 1 0.000101
palma de mallorca 1 0.000101
Gipuzkua 1 0.000101
Garnada 1 0.000101
Las Palma 1 0.000101
vigo 1 0.000101
BARCO DE AVILA (AVILA) 1 0.000101
a Coruña 1 0.000101
rARRAGONA 1 0.000101
Zarago 1 0.000101
Guipozcoa 1 0.000101
New York 1 0.000101
ILLES 1 0.000101
CORU?A,A 1 0.000101
araba 1 0.000101
Santa Cruz De Tenerfie 1 0.000101
ARONA 1 0.000101
BIZKAYA 1 0.000101
Tarrragona 1 0.000101
A CORUA 1 0.000101
AUSTURIAS 1 0.000101
MALLORCA -BALEARES 1 0.000101
Loire-Atlantique 1 0.000101
Montcada I Reixac 1 0.000101
CORBOBA 1 0.000101
Illes Belears 1 0.000101
VIZAYA 1 0.000101
Santa Cruz De Tenerife Canarias 1 0.000101
albacete 1 0.000101
BURGS 1 0.000101
Santa Cruz sde Tenerife 1 0.000101
Todelo 1 0.000101
GUIPúZCOA 1 0.000101
IILES BALEARS 1 0.000101
SANTA CRUZ DE TENERIFE DE TENERIFE 1 0.000101
PEILAGOS 1 0.000101
PICAXEN 1 0.000101
PONTVEDRRDA 1 0.000101
MIERES 1 0.000101
EL HIERRO 1 0.000101
TELDE 1 0.000101
BURJASOL 1 0.000101
PLASENCIA 1 0.000101
ZARAGONA 1 0.000101
BAEARES 1 0.000101
SANT CRUZ DE TENERIFE 1 0.000101
BIZAKAIA 1 0.000101
BENALMADENA 1 0.000101
GUIPUCOA 1 0.000101
ARABA ALAVA 1 0.000101
ARAGON 1 0.000101
CASTELLÓN DE LA PLANA 1 0.000101
ALGUAZAS 1 0.000101
JAéN 1 0.000101
CORRALEJOS 1 0.000101
SUECA 1 0.000101
VICTORIA 1 0.000101
SAN SESBAST 1 0.000101
LA LAGUNA TENERIFE 1 0.000101
GRANJA 1 0.000101
Bsarcelona 1 0.000101
Vizacaya 1 0.000101
ELCHE 1 0.000101
guipzkoa 1 0.000101
FUERTE VENTURA 1 0.000101
BADALONA 1 0.000101
J 1 0.000101
APOLA 1 0.000101
PALMA DE GRAN CANARIA 1 0.000101
S/C DE TENERIFE 1 0.000101
TARRAGON 1 0.000101
LEOn 1 0.000101
Gudalajara 1 0.000101
SANTA CRUZ DE TENERIFA 1 0.000101
Castello 1 0.000101
BALERAES 1 0.000101
EL VERGEL 1 0.000101
Skåne 1 0.000101
Castellón/Castello 1 0.000101
Guipuzcuoa 1 0.000101
GUIPOZ 1 0.000101
LA RIJOA 1 0.000101
GUPUZCOA 1 0.000101
ANDALUCÍA 1 0.000101
Álava 1 0.000101
ALMERíA 1 0.000101
ALBECETE 1 0.000101
BIzkaia 1 0.000101
Francia 1 0.000101
MurciA 1 0.000101
VIZCAA 1 0.000101
Castellón/Castelló 1 0.000101
Almería 1 0.000101
CASTELLO N 1 0.000101
TARAGONA 1 0.000101
PALMAS,LAS 1 0.000101
VIZACAYA 1 0.000101
UTRERA 1 0.000101
VIZKAIA 1 0.000101
PONTEVENDRA 1 0.000101
NERIDA 1 0.000101
LA PALMAS 1 0.000101
STA DE CRUZ DE TENERIFE 1 0.000101
CantabrIa 1 0.000101
a coruñpa 1 0.000101
VALLADOLIS 1 0.000101
VALLADALID 1 0.000101
VALENIA 1 0.000101
GUIPOCUA 1 0.000101
EL HIERRO SANTA CRUZ DE TENERIFE 1 0.000101
EL HIERRO CANARIAS 1 0.000101
Coto de Bornos 1 0.000101
PASCO VASCO 1 0.000101
Mayorca 1 0.000101
EVILLA 1 0.000101
Cádiaz 1 0.000101
DENIA 1 0.000101
VITORIA GASTEIZ 1 0.000101
STA LUCIA TIRAJANAGRAN CANARIA 1 0.000101
TENERIFE CANARIAS 1 0.000101
VIZCVAYA 1 0.000101
gRANADA 1 0.000101
GRANDA 1 0.000101
FRONSAC 1 0.000101
MOTRIL 1 0.000101
CAMAS 1 0.000101
guipuzcua 1 0.000101
MUERCIA 1 0.000101
POLA DE LENA-ASTURIAS 1 0.000101
CATALUÑA 1 0.000101
AlIcante 1 0.000101
VALLODOLID 1 0.000101
avenida de francia 60,c portal 6 3º d 1 0.000101
SEVILLLA 1 0.000101
LA LAGUNA STA CRUZ DE TENERIFE 1 0.000101
ZARAUTZ 1 0.000101
CASTILLA-LA MANCHA 1 0.000101
GRANADAS 1 0.000101
EL FRANCO ASTURIAS 1 0.000101
SANTA CURZ DE TENERIFE 1 0.000101
CASTILLA DE LEON 1 0.000101
GIPUZCUA 1 0.000101
GUALAJARA 1 0.000101
GUIPUZOCA 1 0.000101
GuIpUzcoa 1 0.000101
ANDALUCIA 1 0.000101
LA ALBERCA 1 0.000101
Bizckai 1 0.000101
MAGALA 1 0.000101
Alicate 1 0.000101
Turias 1 0.000101
Vizvaya 1 0.000101
Arona 1 0.000101
Murica 1 0.000101
Lerida 1 0.000101
Viscaya 1 0.000101
Valldolid 1 0.000101
Vlencia 1 0.000101
Andorra 1 0.000101
Andalucia 1 0.000101
MALPICA DE BERGANTIÑOS 1 0.000101
badajod 1 0.000101
Chiang Mai 1 0.000101
HUEVA 1 0.000101
Palma 1 0.000101
GUIPUZCOU 1 0.000101
NAVARRO 1 0.000101
BizKaia 1 0.000101
Fontanarejo 1 0.000101
Mälaga 1 0.000101
SANTA EULALIA DEL RIO 1 0.000101
ZAROGAZA 1 0.000101
Matarrubia 1 0.000101
soria 1 0.000101
Buenos Aires 1 0.000101
Wakiso District 1 0.000101
Las Palmas Telde 1 0.000101
ILES BALEARS 1 0.000101
FUERTEVENTURA 1 0.000101
Valéncia 1 0.000101
CANTAMBRIA 1 0.000101
Valencia/Val?ncia 1 0.000101
SANTA CRUZ DE TENERFIE 1 0.000101
ISLAS BALERES 1 0.000101
GUIPUIZCOA 1 0.000101
LA CARUÑA 1 0.000101
LAS PALMAS (LANZAROTE) 1 0.000101
PATERNA 1 0.000101
VALLADOLIDAD 1 0.000101
GUIPOZKOA 1 0.000101
FELANITX 1 0.000101
LleIda 1 0.000101
La Coru?a 1 0.000101
SAN SE BASTIAN 1 0.000101
PONTEVERA 1 0.000101
CALLELLON 1 0.000101
CIUDAR REAL 1 0.000101
BRION 1 0.000101
baleares 1 0.000101
CUDAD REAL 1 0.000101
STA CRUZ DE TERENIFE 1 0.000101
LERIDA/LLEIDA 1 0.000101
BEJAR 1 0.000101
GUIPOZCOA 1 0.000101
La Pama 1 0.000101
PONFERRADA 1 0.000101
Astudias 1 0.000101
MelillA 1 0.000101
Hertfordshire 1 0.000101
Munchen 1 0.000101
SANTA CRUZ DE La PALMA 1 0.000101
Sta Cruz De Tenerife 1 0.000101
lanzarote 1 0.000101
Tarrgona 1 0.000101
SUIZA 1 0.000101
La rioja 1 0.000101
Bogotà 1 0.000101
Virginia 1 0.000101
ILLES BALEARS MENORCA 1 0.000101
Castilla y León 1 0.000101
ALMERiA 1 0.000101
GUIPIZCOA 1 0.000101
Niedersachsen 1 0.000101
BARCELONAc23090 1 0.000101
Alicane 1 0.000101
Alicante/Alcant 1 0.000101
VILLANUEVA DE AROSA 1 0.000101
AQUITANIA 1 0.000101
Castellóna 1 0.000101
PORTUGAL 1 0.000101
FERROL 1 0.000101
Toledo. 1 0.000101
CASTELLO 1 0.000101
Valrencia 1 0.000101
Terrassa 1 0.000101
Hauts de Seine 1 0.000101
Fuengirola 1 0.000101
MÉRIDA 1 0.000101
S.C. TENERIFE 1 0.000101
Islas Balears 1 0.000101
PAMPLOANA 1 0.000101
GuipuzcoA 1 0.000101
BAdAjoz 1 0.000101
LAs PAlmAs 1 0.000101
A Coruna 1 0.000101
sant sadurni de noia 1 0.000101
San Sebastin 1 0.000101
España 1 0.000101
AlbAcete 1 0.000101
Albacte 1 0.000101
Cieza 1 0.000101
SALALANCA 1 0.000101
ALVA 1 0.000101
Castellón de la Plana 1 0.000101
VALENCA 1 0.000101
CAstellOn/CAstellO 1 0.000101
Guipúscoa 1 0.000101
Pontvendra 1 0.000101
Santa Cruz de Tenerifie 1 0.000101
Rioja,la 1 0.000101
Vizcaia 1 0.000101
XATIVA 1 0.000101
Luego 1 0.000101
Badiajoz 1 0.000101
Algeciras 1 0.000101
TARRAGORRA 1 0.000101
YEIDA 1 0.000101
Barcelna 1 0.000101
Las Baleares 1 0.000101
Guipuzkuo 1 0.000101
Gupuzcoa 1 0.000101
ARRASATE/MONDRAGON 1 0.000101
Sta.cruz Tenerife 1 0.000101
Barelona 1 0.000101
VAlencia 1 0.000101
Catarroja 1 0.000101
Castellon/Castelló 1 0.000101
Albate 1 0.000101
Navara 1 0.000101
Las Palmas - Telde 1 0.000101
LAS PALMAS DE GRAN CANARIOS 1 0.000101
gerona 1 0.000101
Schwieberdingen 1 0.000101
Iiles Balears 1 0.000101
Aragón 1 0.000101
Alicante. 1 0.000101
TARRAGAONA 1 0.000101
A 1 0.000101
CALVIA 1 0.000101
PALMA DE MALORCA 1 0.000101
LAS PALMAS GRAN CANARIAS 1 0.000101
LANZARATE 1 0.000101
islas Baleares 1 0.000101
ON 1 0.000101
Castilla y la Mancha 1 0.000101
Sant vicente 1 0.000101
Cordoba Ibarruri 3 esc 1 3 1 1 0.000101
Corboda 1 0.000101
STOCKHOLM 1 0.000101
PARIS 1 0.000101
Paris 1 0.000101
VICTORAI GAXTEIZ 1 0.000101
BAYONA 1 0.000101
LE0N 1 0.000101
LAS PALMAS GRAN CANARIA 1 0.000101
bizcaia 1 0.000101
VILLAPEDRE 1 0.000101
VAL DE MARNE 1 0.000101
BABIERA 1 0.000101
SANTA CRUZ DE LA PALMA 1 0.000101
Arava 1 0.000101
GRAN CANARIAS - LAS PALMAS 1 0.000101
A CORUNA 1 0.000101
KINSHASA 1 0.000101
Centro 1 0.000101
ORENZE 1 0.000101
Lagunes 1 0.000101
Leste 1 0.000101
Sud -Kivu 1 0.000101
NORD KIVU 1 0.000101
Zinder 1 0.000101
Madrdi 1 0.000101
Elfashir 1 0.000101
gipuzkoa 1 0.000101
mailingstate: provincia.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npsp__largest_soft_credit_amount__c: Variable numerica
In [691]:
# Vamos a realizar analisis por cada variable
var = "npsp__largest_soft_credit_amount__c"
In [692]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__largest_soft_credit_amount__c es 994064. Lo que supone un 100.0%
El nº de vacios para la variable npsp__largest_soft_credit_amount__c es 0. Lo que supone un 0.0%
Out[692]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'npsp__largest_soft_credit_amount__c']
In [693]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[693]:
# Tot % Tot
npsp__largest_soft_credit_amount__c: mayor importe de operaciones indirectas.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npo02__soft_credit_last_year__c: Variable numerica
In [694]:
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_last_year__c"
In [695]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__soft_credit_last_year__c es 994064. Lo que supone un 100.0%
El nº de vacios para la variable npo02__soft_credit_last_year__c es 0. Lo que supone un 0.0%
Out[695]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c']
In [696]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[696]:
# Tot % Tot
npo02__soft_credit_last_year__c: operaciones indirectas el año pasado.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npo02__soft_credit_this_year__c: Variable numerica
In [697]:
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_this_year__c"
In [698]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__soft_credit_this_year__c es 994064. Lo que supone un 100.0%
El nº de vacios para la variable npo02__soft_credit_this_year__c es 0. Lo que supone un 0.0%
Out[698]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c']
In [699]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[699]:
# Tot % Tot
npo02__soft_credit_this_year__c: operaciones indirectas este año.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npo02__soft_credit_two_years_ago__c: Variable numerica
In [700]:
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_two_years_ago__c"
In [701]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__soft_credit_two_years_ago__c es 994064. Lo que supone un 100.0%
El nº de vacios para la variable npo02__soft_credit_two_years_ago__c es 0. Lo que supone un 0.0%
Out[701]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c']
In [702]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[702]:
# Tot % Tot
npo02__soft_credit_two_years_ago__c: operaciones indirectas hace 2 años.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_nocaptacionfondoscp__c: Variable booleana
In [703]:
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondoscp__c"
In [704]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondoscp__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_nocaptacionfondoscp__c es 0. Lo que supone un 0.0%
In [705]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[705]:
# Tot % Tot
False 820400 82.529897
True 173664 17.470103
msf_nocaptacionfondoscp__c: permiso de comuncacion por correo postal.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_nocaptacionfondosemail__c: Variable booleana
In [706]:
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondosemail__c"
In [707]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondosemail__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_nocaptacionfondosemail__c es 0. Lo que supone un 0.0%
In [708]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[708]:
# Tot % Tot
False 852927 85.802021
True 141137 14.197979
msf_nocaptacionfondosemail__c: permiso de comuncacion por email.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_nocaptacionfondosmi__c: Variable booleana
In [709]:
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondosmi__c"
In [710]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondosmi__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_nocaptacionfondosmi__c es 0. Lo que supone un 0.0%
In [711]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[711]:
# Tot % Tot
False 886401 89.16941
True 107663 10.83059
msf_nocaptacionfondosmi__c: permiso de comuncacion por mi.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_nocaptacionfondossms__c: Variable booleana
In [712]:
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondossms__c"
In [713]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondossms__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_nocaptacionfondossms__c es 0. Lo que supone un 0.0%
In [714]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[714]:
# Tot % Tot
False 884392 88.96731
True 109672 11.03269
msf_nocaptacionfondossms__c: permiso de comuncacion por sms.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_firstcampaignentryrecurringdonor__c: Variable categorica
In [715]:
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaignentryrecurringdonor__c"
In [716]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstcampaignentryrecurringdonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_firstcampaignentryrecurringdonor__c es 589. Lo que supone un 0.05925171819923063%
In [717]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[717]:
# Tot % Tot
7013Y000001mr4CQAQ 37787 3.801264
7013Y000001mr2DQAQ 31300 3.148691
7013Y000001mr2cQAA 26419 2.657676
7013Y000001mrCzQAI 25969 2.612407
7013Y000001mrBSQAY 24008 2.415136
... ... ...
7013Y000001mrOuQAI 1 0.000101
7013Y000001mrGjQAI 1 0.000101
7013Y000001mrUjQAI 1 0.000101
7013Y000001mqxTQAQ 1 0.000101
7013Y000001mre3QAA 1 0.000101

2565 rows × 2 columns

msf_firstcampaignentryrecurringdonor__c: primera campaña de colaboracion como socio recurrente.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_firstcampaingcolaboration__c: Variable categorica
In [718]:
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaingcolaboration__c"
In [719]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstcampaingcolaboration__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_firstcampaingcolaboration__c es 45219. Lo que supone un 4.548902283957572%
In [720]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[720]:
# Tot % Tot
45219 4.548902
7013Y000001mrCzQAI 38402 3.863132
7013Y000001mr4CQAQ 34914 3.512249
7013Y000001mr2DQAQ 27073 2.723466
7013Y000001mr2cQAA 23877 2.401958
... ... ...
7013Y000001mqs2QAA 1 0.000101
7013Y000001mrO2QAI 1 0.000101
7013Y000001mquxQAA 1 0.000101
7013Y000001mqzYQAQ 1 0.000101
7013Y000001mqyeQAA 1 0.000101

2742 rows × 2 columns

msf_firstcampaingcolaboration__c: primera campaña de colaboracion economica.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_firstannualizedquota__c: Variable numerica
In [721]:
# Vamos a realizar analisis por cada variable
var = "msf_firstannualizedquota__c"
In [722]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstannualizedquota__c es 32507. Lo que supone un 3.270111381158557%
El nº de vacios para la variable msf_firstannualizedquota__c es 0. Lo que supone un 0.0%
In [723]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[723]:
# Tot % Tot
1.200000e+02 287648 29.914815
6.000000e+01 123651 12.859456
1.800000e+02 103083 10.720425
2.400000e+02 51256 5.330521
7.200000e+01 49342 5.131469
1.440000e+02 42521 4.422099
7.212000e+01 32909 3.422470
3.600000e+01 25308 2.631981
3.600000e+02 18085 1.880804
9.600000e+01 16441 1.709831
3.000000e+02 14049 1.461068
1.000000e+02 13112 1.363622
5.000000e+01 11678 1.214489
5.196000e+01 11000 1.143978
0.000000e+00 9343 0.971653
4.000000e+01 8834 0.918718
6.010000e+01 8598 0.894175
8.400000e+01 7852 0.816592
3.005000e+01 7602 0.790593
2.000000e+01 7382 0.767713
8.000000e+01 7025 0.730586
3.000000e+01 6974 0.725282
1.202000e+02 6514 0.677443
1.442400e+02 5434 0.565125
4.800000e+01 5204 0.541206
2.163600e+02 4876 0.507094
2.000000e+02 4820 0.501270
6.000000e+02 4229 0.439808
3.606000e+02 3825 0.397792
1.200000e+01 3772 0.392280
1.000000e+01 3465 0.360353
1.500000e+02 3268 0.339865
1.803000e+01 3166 0.329258
1.320000e+02 2999 0.311890
2.160000e+02 2293 0.238467
1.500000e+01 2293 0.238467
7.200000e+02 1971 0.204980
9.015000e+01 1774 0.184492
2.404000e+02 1725 0.179397
2.500000e+01 1672 0.173885
1.080000e+02 1569 0.163173
9.000000e+01 1565 0.162757
4.800000e+02 1354 0.140813
4.808000e+01 1208 0.125630
2.400000e+01 1120 0.116478
1.200000e+03 1049 0.109094
2.404000e+01 1043 0.108470
3.486000e+01 1041 0.108262
1.600000e+02 939 0.097654
1.560000e+02 856 0.089022
2.040000e+02 850 0.088398
4.000000e+02 814 0.084654
1.502500e+02 781 0.081222
7.212000e+02 779 0.081014
1.394400e+02 744 0.077375
3.606000e+01 724 0.075295
3.612000e+01 713 0.074151
1.082400e+02 652 0.067807
1.920000e+02 627 0.065207
1.040400e+02 598 0.062191
7.000000e+01 534 0.055535
1.803600e+02 503 0.052311
7.500000e+01 439 0.045655
6.010000e+00 382 0.039727
2.500000e+02 377 0.039207
1.730400e+02 376 0.039103
1.680000e+02 376 0.039103
4.200000e+02 353 0.036711
9.316000e+01 344 0.035775
1.202000e+01 341 0.035463
5.000000e+02 306 0.031823
2.884800e+02 304 0.031615
1.039200e+02 287 0.029847
5.000000e+00 273 0.028391
2.520000e+02 270 0.028079
9.616000e+01 262 0.027247
3.608000e+01 260 0.027039
7.224000e+01 254 0.026415
2.640000e+02 248 0.025792
1.803000e+02 248 0.025792
5.768000e+01 243 0.025272
2.880000e+02 243 0.025272
1.400000e+02 240 0.024960
5.200000e+01 229 0.023816
3.005100e+02 204 0.021216
4.183200e+02 204 0.021216
1.800000e+01 193 0.020072
1.000000e+03 177 0.018408
3.000000e+00 176 0.018304
6.000000e+00 146 0.015184
3.500000e+01 141 0.014664
6.012000e+01 141 0.014664
5.400000e+02 140 0.014560
2.885000e+01 138 0.014352
1.800000e+03 138 0.014352
4.207000e+01 136 0.014144
3.200000e+01 133 0.013832
4.320000e+02 116 0.012064
1.154000e+02 114 0.011856
1.250000e+02 112 0.011648
3.462000e+02 108 0.011232
1.442000e+01 101 0.010504
8.414000e+01 100 0.010400
1.080000e+03 99 0.010296
4.500000e+01 95 0.009880
1.923200e+02 89 0.009256
1.081800e+03 86 0.008944
5.770000e+01 86 0.008944
5.409000e+01 80 0.008320
2.400000e+03 80 0.008320
4.327200e+02 77 0.008008
8.000000e+02 75 0.007800
4.200000e+01 75 0.007800
6.010000e+02 71 0.007384
9.600000e+02 70 0.007280
6.010100e+02 68 0.007072
1.300000e+02 66 0.006864
9.000000e+02 64 0.006656
8.400000e+02 62 0.006448
3.960000e+02 61 0.006344
4.808000e+02 61 0.006344
2.760000e+02 60 0.006240
1.440000e+03 60 0.006240
8.000000e+00 60 0.006240
3.120000e+02 59 0.006136
1.500000e+03 55 0.005720
5.500000e+01 54 0.005616
1.081800e+02 53 0.005512
3.726400e+02 52 0.005408
5.769600e+02 48 0.004992
1.100000e+02 47 0.004888
1.204000e+01 46 0.004784
3.600000e+03 46 0.004784
1.803200e+02 45 0.004680
1.682800e+02 45 0.004680
2.404100e+02 43 0.004472
3.614400e+02 41 0.004264
3.240000e+02 41 0.004264
6.500000e+01 40 0.004160
3.200000e+02 39 0.004056
5.048400e+02 39 0.004056
1.442400e+03 39 0.004056
2.524800e+02 39 0.004056
5.400000e+01 38 0.003952
2.280000e+02 38 0.003952
3.840000e+02 37 0.003848
1.600000e+01 36 0.003744
8.654400e+02 36 0.003744
2.800000e+02 36 0.003744
1.082000e+02 36 0.003744
8.460000e+01 36 0.003744
2.000000e+03 33 0.003432
9.020000e+00 33 0.003432
2.200000e+02 32 0.003328
2.800000e+01 32 0.003328
3.650000e+02 30 0.003120
1.204800e+02 30 0.003120
3.360000e+02 30 0.003120
3.000000e+03 30 0.003120
3.500000e+02 29 0.003016
1.040000e+02 29 0.003016
1.094400e+02 27 0.002808
6.924000e+02 26 0.002704
6.000000e+03 26 0.002704
8.416000e+01 25 0.002600
3.720000e+02 25 0.002600
3.606100e+02 24 0.002496
7.813000e+01 24 0.002496
8.800000e+01 24 0.002496
5.040000e+02 23 0.002392
1.700000e+02 23 0.002392
2.600000e+02 23 0.002392
1.803000e+03 23 0.002392
6.024000e+01 22 0.002288
6.600000e+01 22 0.002288
5.600000e+01 22 0.002288
1.503000e+01 22 0.002288
3.900000e+01 21 0.002184
3.010000e+00 20 0.002080
4.680000e+02 20 0.002080
9.200000e+01 18 0.001872
2.160000e+01 18 0.001872
1.750000e+02 18 0.001872
3.800000e+01 18 0.001872
8.652000e+01 17 0.001768
1.824000e+02 17 0.001768
6.600000e+02 17 0.001768
8.500000e+01 16 0.001664
2.308000e+02 16 0.001664
2.103500e+02 16 0.001664
7.800000e+01 16 0.001664
4.400000e+01 16 0.001664
4.080000e+02 15 0.001560
1.520000e+02 15 0.001560
6.800000e+01 14 0.001456
8.640000e+02 14 0.001456
6.400000e+01 13 0.001352
3.012000e+01 13 0.001352
3.005000e+02 13 0.001352
1.200000e+04 13 0.001352
1.400000e+01 13 0.001352
6.120000e+02 13 0.001352
2.200000e+01 13 0.001352
1.201200e+02 12 0.001248
6.240000e+02 12 0.001248
7.000000e+00 12 0.001248
1.020000e+02 12 0.001248
4.000000e+00 12 0.001248
4.500000e+02 12 0.001248
1.719600e+02 12 0.001248
9.036000e+01 11 0.001144
3.606120e+03 11 0.001144
1.480000e+02 11 0.001144
5.760000e+02 11 0.001144
7.200000e+00 11 0.001144
1.202040e+03 11 0.001144
7.600000e+01 10 0.001040
4.332000e+01 10 0.001040
1.120000e+02 10 0.001040
7.920000e+02 10 0.001040
7.210000e+00 10 0.001040
9.012000e+01 9 0.000936
1.450000e+02 9 0.000936
4.508000e+01 9 0.000936
2.600000e+01 9 0.000936
1.280000e+02 9 0.000936
3.005200e+02 9 0.000936
3.480000e+02 9 0.000936
2.160000e+03 9 0.000936
9.000000e+00 8 0.000832
7.400000e+01 8 0.000832
5.289000e+01 8 0.000832
3.400000e+01 8 0.000832
7.228800e+02 8 0.000832
2.100000e+02 8 0.000832
7.800000e+02 7 0.000728
5.772000e+01 7 0.000728
6.200000e+01 7 0.000728
6.490800e+02 7 0.000728
7.300000e+01 7 0.000728
1.300000e+01 7 0.000728
5.300000e+01 7 0.000728
4.560000e+02 7 0.000728
3.365600e+02 7 0.000728
1.050000e+02 7 0.000728
6.611000e+01 7 0.000728
1.117920e+03 7 0.000728
1.350000e+02 7 0.000728
9.016000e+01 6 0.000624
1.160000e+02 6 0.000624
4.800000e+03 6 0.000624
1.020000e+03 6 0.000624
1.444000e+01 6 0.000624
7.932000e+01 6 0.000624
7.000000e+02 6 0.000624
5.052000e+01 6 0.000624
5.200000e+02 6 0.000624
2.250000e+02 6 0.000624
1.600000e+03 6 0.000624
2.163600e+03 6 0.000624
2.880000e+01 6 0.000624
7.212200e+02 6 0.000624
1.000000e+00 6 0.000624
3.700000e+01 5 0.000520
9.360000e+02 5 0.000520
9.996000e+01 5 0.000520
2.300000e+02 5 0.000520
1.100000e+01 5 0.000520
1.920000e+03 5 0.000520
1.240000e+02 5 0.000520
7.200000e-01 5 0.000520
1.320000e+03 5 0.000520
9.900000e+01 5 0.000520
5.409600e+02 5 0.000520
2.164000e+01 5 0.000520
1.440000e+01 5 0.000520
9.015200e+02 5 0.000520
7.200000e+03 5 0.000520
2.115600e+02 5 0.000520
4.400000e+02 4 0.000416
9.375600e+02 4 0.000416
1.560000e+03 4 0.000416
5.000000e+03 4 0.000416
1.360000e+02 4 0.000416
1.620000e+02 4 0.000416
9.496000e+01 4 0.000416
5.592000e+01 4 0.000416
4.507600e+02 4 0.000416
3.300000e+01 4 0.000416
1.700000e+01 4 0.000416
2.700000e+01 4 0.000416
5.412000e+01 4 0.000416
1.260000e+02 4 0.000416
6.400000e+02 4 0.000416
1.081200e+02 4 0.000416
1.400000e+03 4 0.000416
8.660000e+00 4 0.000416
1.250000e+01 4 0.000416
2.100000e+01 4 0.000416
2.404040e+03 4 0.000416
1.650000e+02 4 0.000416
2.409600e+02 4 0.000416
7.560000e+02 4 0.000416
1.502400e+02 4 0.000416
2.700000e+02 4 0.000416
2.300000e+01 4 0.000416
4.330000e+00 4 0.000416
4.000000e+03 3 0.000312
3.100000e+01 3 0.000312
4.207100e+02 3 0.000312
4.328000e+01 3 0.000312
1.081840e+03 3 0.000312
2.705000e+01 3 0.000312
9.616400e+02 3 0.000312
5.202000e+01 3 0.000312
7.500000e+02 3 0.000312
2.040000e+03 3 0.000312
1.226400e+02 3 0.000312
3.900000e+03 3 0.000312
2.104000e+01 3 0.000312
1.129900e+02 3 0.000312
7.513000e+01 3 0.000312
2.884920e+03 3 0.000312
1.009680e+03 3 0.000312
3.004000e+01 3 0.000312
3.330000e+02 3 0.000312
6.972000e+01 3 0.000312
3.996000e+01 3 0.000312
1.804000e+01 3 0.000312
1.803040e+03 3 0.000312
3.846400e+02 3 0.000312
2.884000e+01 3 0.000312
2.750000e+02 3 0.000312
4.440000e+02 3 0.000312
1.732000e+01 3 0.000312
1.212000e+03 3 0.000312
1.510000e+02 3 0.000312
3.125200e+02 3 0.000312
2.004000e+03 3 0.000312
3.400000e+02 3 0.000312
1.900000e+02 3 0.000312
6.360000e+02 3 0.000312
9.372000e+01 3 0.000312
5.100000e+01 3 0.000312
1.983300e+02 3 0.000312
6.480000e+02 3 0.000312
1.532600e+02 3 0.000312
1.983600e+02 3 0.000312
4.600000e+02 2 0.000208
1.800000e+04 2 0.000208
3.750000e+02 2 0.000208
5.988000e+01 2 0.000208
3.660000e+02 2 0.000208
1.280200e+02 2 0.000208
7.356000e+02 2 0.000208
3.666000e+01 2 0.000208
7.933200e+02 2 0.000208
2.884900e+02 2 0.000208
1.830000e+02 2 0.000208
1.850000e+02 2 0.000208
1.210000e+02 2 0.000208
4.800000e+00 2 0.000208
2.480000e+02 2 0.000208
4.600000e+01 2 0.000208
8.414000e+02 2 0.000208
1.322400e+02 2 0.000208
4.327000e+01 2 0.000208
1.200100e+02 2 0.000208
6.100000e+01 2 0.000208
2.644400e+02 2 0.000208
6.492000e+01 2 0.000208
1.640000e+02 2 0.000208
4.920000e+02 2 0.000208
5.500000e+02 2 0.000208
3.250000e+02 2 0.000208
2.520000e+01 2 0.000208
8.200000e+01 2 0.000208
1.502600e+02 2 0.000208
2.406000e+02 2 0.000208
7.440000e+01 2 0.000208
1.010000e+02 2 0.000208
6.500000e+02 2 0.000208
2.019600e+02 2 0.000208
2.403600e+02 2 0.000208
3.602400e+02 2 0.000208
4.200000e+03 2 0.000208
1.622400e+02 2 0.000208
8.700000e+01 2 0.000208
7.200000e+04 2 0.000208
3.006000e+01 2 0.000208
3.300000e+02 2 0.000208
1.802800e+02 2 0.000208
2.598000e+01 2 0.000208
9.999600e+02 2 0.000208
2.000000e+00 2 0.000208
7.700000e+01 2 0.000208
9.320000e+00 2 0.000208
2.900000e+01 2 0.000208
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1.202020e+03 2 0.000208
1.021700e+02 2 0.000208
4.808100e+02 2 0.000208
1.150000e+02 2 0.000208
9.500000e+01 2 0.000208
1.959600e+02 2 0.000208
5.520000e+02 2 0.000208
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1.008000e+03 2 0.000208
1.230000e+02 2 0.000208
1.586400e+02 2 0.000208
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1.382300e+02 2 0.000208
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7.212120e+03 2 0.000208
1.444800e+02 2 0.000208
4.300000e+01 2 0.000208
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1.740000e+02 2 0.000208
5.880000e+02 2 0.000208
8.656000e+01 2 0.000208
7.452000e+01 2 0.000208
4.320000e+03 2 0.000208
1.394000e+02 2 0.000208
7.320000e+01 2 0.000208
2.220000e+02 2 0.000208
8.040000e+02 2 0.000208
6.200000e+02 2 0.000208
7.320000e+02 2 0.000208
1.100000e+03 2 0.000208
7.440000e+02 2 0.000208
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3.607200e+02 2 0.000208
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3.060000e+02 2 0.000208
1.000100e+02 2 0.000208
1.200000e+00 2 0.000208
2.440000e+02 2 0.000208
1.355880e+03 2 0.000208
1.684000e+01 2 0.000208
1.960000e+02 2 0.000208
1.562800e+02 2 0.000208
9.100000e+01 2 0.000208
2.061500e+02 2 0.000208
5.408000e+01 2 0.000208
7.992000e+01 2 0.000208
1.250000e+03 2 0.000208
2.880000e+03 2 0.000208
5.196000e+02 2 0.000208
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6.130800e+02 2 0.000208
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1.154400e+02 2 0.000208
3.246000e+02 2 0.000208
2.379600e+02 2 0.000208
1.262100e+02 2 0.000208
2.550000e+02 1 0.000104
8.640000e+01 1 0.000104
9.204000e+01 1 0.000104
5.280000e+02 1 0.000104
9.999000e+01 1 0.000104
5.600000e+02 1 0.000104
4.692000e+01 1 0.000104
5.280000e+01 1 0.000104
6.840000e+02 1 0.000104
9.324000e+01 1 0.000104
5.160000e+01 1 0.000104
6.060000e+01 1 0.000104
2.240000e+02 1 0.000104
6.000000e-01 1 0.000104
6.015000e+01 1 0.000104
9.840000e+03 1 0.000104
2.476800e+02 1 0.000104
3.110000e+02 1 0.000104
6.600000e+03 1 0.000104
6.800000e+02 1 0.000104
2.100000e+03 1 0.000104
2.560000e+02 1 0.000104
1.716000e+02 1 0.000104
9.015100e+02 1 0.000104
3.010000e+02 1 0.000104
1.900000e+01 1 0.000104
2.150000e+02 1 0.000104
5.800000e+01 1 0.000104
1.202400e+02 1 0.000104
5.908000e+01 1 0.000104
1.680000e+03 1 0.000104
1.665600e+02 1 0.000104
1.250400e+02 1 0.000104
1.159200e+02 1 0.000104
6.235200e+02 1 0.000104
1.442440e+03 1 0.000104
2.720000e+02 1 0.000104
2.439600e+02 1 0.000104
3.800000e+02 1 0.000104
1.000800e+02 1 0.000104
5.040000e+01 1 0.000104
3.350000e+02 1 0.000104
2.253800e+03 1 0.000104
3.040000e+01 1 0.000104
1.052400e+02 1 0.000104
1.893600e+02 1 0.000104
1.446000e+02 1 0.000104
5.100000e+02 1 0.000104
1.296000e+03 1 0.000104
5.700000e+01 1 0.000104
2.560000e+01 1 0.000104
3.320000e+02 1 0.000104
1.812000e+02 1 0.000104
3.726000e+01 1 0.000104
2.960000e+02 1 0.000104
1.470000e+02 1 0.000104
1.860000e+03 1 0.000104
5.288000e+01 1 0.000104
1.140000e+03 1 0.000104
6.720000e+01 1 0.000104
6.876000e+01 1 0.000104
9.912000e+01 1 0.000104
1.658400e+02 1 0.000104
2.999000e+01 1 0.000104
1.238000e+02 1 0.000104
1.452000e+02 1 0.000104
1.208000e+02 1 0.000104
2.050000e+02 1 0.000104
2.000400e+02 1 0.000104
6.016000e+01 1 0.000104
4.208000e+01 1 0.000104
2.180000e+02 1 0.000104
4.100000e+01 1 0.000104
1.002000e+03 1 0.000104
7.812000e+01 1 0.000104
3.954800e+02 1 0.000104
3.005060e+04 1 0.000104
2.920000e+02 1 0.000104
1.472500e+02 1 0.000104
1.478520e+03 1 0.000104
6.346800e+02 1 0.000104
4.095600e+02 1 0.000104
2.496000e+03 1 0.000104
4.992000e+01 1 0.000104
6.001000e+01 1 0.000104
5.900000e+01 1 0.000104
1.586640e+03 1 0.000104
4.700000e+01 1 0.000104
1.056000e+02 1 0.000104
1.340000e+02 1 0.000104
8.246000e+02 1 0.000104
1.089600e+02 1 0.000104
1.947600e+02 1 0.000104
2.310000e+02 1 0.000104
6.660000e+01 1 0.000104
4.116000e+01 1 0.000104
1.300000e+03 1 0.000104
3.768000e+02 1 0.000104
2.340000e+02 1 0.000104
1.420000e+02 1 0.000104
2.388000e+02 1 0.000104
2.850000e+02 1 0.000104
3.780000e+02 1 0.000104
9.400000e+01 1 0.000104
1.036800e+02 1 0.000104
3.906600e+02 1 0.000104
4.928400e+02 1 0.000104
1.080000e+01 1 0.000104
5.048000e+01 1 0.000104
9.600000e+00 1 0.000104
1.380000e+03 1 0.000104
9.720000e+01 1 0.000104
9.096000e+01 1 0.000104
1.002000e+02 1 0.000104
1.870000e+02 1 0.000104
1.027200e+02 1 0.000104
9.232000e+01 1 0.000104
1.268400e+02 1 0.000104
3.885000e+01 1 0.000104
1.298400e+02 1 0.000104
5.160000e+02 1 0.000104
3.305600e+02 1 0.000104
3.336000e+02 1 0.000104
1.033760e+03 1 0.000104
2.043600e+02 1 0.000104
1.284000e+03 1 0.000104
5.944800e+02 1 0.000104
4.688400e+02 1 0.000104
1.800000e+00 1 0.000104
7.510000e+00 1 0.000104
6.010200e+02 1 0.000104
9.020000e+01 1 0.000104
3.065200e+02 1 0.000104
4.028000e+01 1 0.000104
8.292000e+01 1 0.000104
2.456676e+07 1 0.000104
2.596800e+02 1 0.000104
1.430400e+02 1 0.000104
2.200000e+03 1 0.000104
6.132000e+01 1 0.000104
1.322200e+02 1 0.000104
2.704600e+02 1 0.000104
7.220000e+01 1 0.000104
1.200000e+05 1 0.000104
1.840000e+02 1 0.000104
1.710000e+02 1 0.000104
1.502530e+03 1 0.000104
1.202000e+03 1 0.000104
4.580000e+02 1 0.000104
5.949600e+02 1 0.000104
2.307600e+02 1 0.000104
5.109000e+01 1 0.000104
3.124000e+01 1 0.000104
3.100000e+02 1 0.000104
6.010120e+03 1 0.000104
1.620000e+03 1 0.000104
2.524400e+02 1 0.000104
1.060000e+02 1 0.000104
6.720000e+02 1 0.000104
9.012000e+02 1 0.000104
2.500000e+03 1 0.000104
9.600000e+03 1 0.000104
4.182000e+02 1 0.000104
1.045760e+03 1 0.000104
9.840000e+02 1 0.000104
1.552000e+01 1 0.000104
2.046000e+02 1 0.000104
1.146720e+03 1 0.000104
1.030000e+02 1 0.000104
1.875600e+02 1 0.000104
9.720000e+02 1 0.000104
9.240000e+00 1 0.000104
3.612000e+03 1 0.000104
2.524300e+02 1 0.000104
2.402000e+01 1 0.000104
1.083600e+02 1 0.000104
1.092000e+02 1 0.000104
6.242400e+02 1 0.000104
5.870000e+00 1 0.000104
2.928000e+02 1 0.000104
1.983200e+02 1 0.000104
2.760000e+03 1 0.000104
8.166000e+03 1 0.000104
2.803600e+02 1 0.000104
1.980000e+02 1 0.000104
1.524000e+03 1 0.000104
1.203000e+01 1 0.000104
8.052648e+09 1 0.000104
1.080000e+06 1 0.000104
1.164000e+06 1 0.000104
7.596000e+01 1 0.000104
2.058000e+04 1 0.000104
1.253400e+02 1 0.000104
5.400000e+03 1 0.000104
1.239600e+02 1 0.000104
1.939200e+02 1 0.000104
9.015240e+03 1 0.000104
2.636000e+01 1 0.000104
7.230000e+01 1 0.000104
1.046400e+02 1 0.000104
6.008000e+01 1 0.000104
1.104000e+02 1 0.000104
4.059600e+02 1 0.000104
2.957040e+03 1 0.000104
7.220000e+00 1 0.000104
1.009200e+02 1 0.000104
4.900000e+01 1 0.000104
2.425000e+02 1 0.000104
4.484000e+01 1 0.000104
5.152800e+02 1 0.000104
8.925000e+01 1 0.000104
2.956800e+02 1 0.000104
2.410000e+02 1 0.000104
7.080000e+02 1 0.000104
2.064000e+03 1 0.000104
1.550000e+02 1 0.000104
1.834800e+02 1 0.000104
2.170800e+02 1 0.000104
2.401200e+02 1 0.000104
9.800000e+01 1 0.000104
3.889200e+02 1 0.000104
2.184000e+03 1 0.000104
4.700000e+02 1 0.000104
1.008000e+04 1 0.000104
1.536000e+03 1 0.000104
1.009600e+02 1 0.000104
5.460000e+01 1 0.000104
1.001500e+02 1 0.000104
1.678800e+02 1 0.000104
6.005000e+01 1 0.000104
1.812000e+03 1 0.000104
1.298160e+03 1 0.000104
1.490400e+02 1 0.000104
3.604000e+01 1 0.000104
3.996000e+02 1 0.000104
7.620000e+01 1 0.000104
3.700000e+02 1 0.000104
9.960000e+01 1 0.000104
8.016000e+01 1 0.000104
1.975200e+02 1 0.000104
6.300000e+01 1 0.000104
2.199720e+03 1 0.000104
1.056000e+03 1 0.000104
1.002800e+02 1 0.000104
1.090000e+02 1 0.000104
1.188000e+02 1 0.000104
6.006000e+01 1 0.000104
1.802400e+02 1 0.000104
1.500000e+04 1 0.000104
1.514400e+02 1 0.000104
6.020000e+02 1 0.000104
1.500100e+02 1 0.000104
3.000100e+02 1 0.000104
2.524200e+03 1 0.000104
2.193600e+02 1 0.000104
2.900000e+02 1 0.000104
1.501500e+02 1 0.000104
2.451600e+02 1 0.000104
5.493000e+01 1 0.000104
8.655000e+01 1 0.000104
1.211640e+03 1 0.000104
2.352000e+03 1 0.000104
2.196000e+02 1 0.000104
1.203600e+02 1 0.000104
3.607000e+01 1 0.000104
8.880000e+02 1 0.000104
1.200800e+02 1 0.000104
8.280000e+02 1 0.000104
7.204000e+01 1 0.000104
1.201200e+04 1 0.000104
8.900000e+01 1 0.000104
3.480000e+03 1 0.000104
3.230000e+02 1 0.000104
1.236000e+03 1 0.000104
4.119600e+02 1 0.000104
1.436400e+02 1 0.000104
1.340000e+01 1 0.000104
2.282400e+03 1 0.000104
5.950000e+01 1 0.000104
4.520000e+02 1 0.000104
6.480000e+01 1 0.000104
1.599600e+02 1 0.000104
1.220000e+02 1 0.000104
msf_firstannualizedquota__c: importe anualizado del primer compromiso como socio.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_program__c: Variable categorica
In [724]:
# Vamos a realizar analisis por cada variable
var = "msf_program__c"
In [725]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_program__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_program__c es 27752. Lo que supone un 2.7917719583447345%
In [726]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[726]:
# Tot % Tot
Reactivación bajas MASS 473018 47.584260
Cultivación socios MASS 432996 43.558161
27752 2.791772
Retención 1r año MASS 24156 2.430025
Cultivación socios MID 17102 1.720412
Empresas y Colectivos Mass 5923 0.595837
Cultivación/conversión Donantes MASS 5512 0.554491
Mid+ Donors 4415 0.444136
Testamentarios 781 0.078566
Otros programas transversales 688 0.069211
Reactivación bajas MID 372 0.037422
Conversión prospectos 218 0.021930
Retención 1r año MID 188 0.018912
Prospectos Empresas & Colectivos Mass 169 0.017001
Cultivación/conversión Donantes MID 143 0.014385
Otros 12Few+ 132 0.013279
Reactivación/conversión EXDonantes MASS 93 0.009356
Empresas y Colectivos Mid, Mid + 86 0.008651
Públicos Especiales 81 0.008148
Potenciales a Major Donors 56 0.005633
Vehículo donación de Gran Donante = YES 55 0.005533
Major Donors 42 0.004225
Instituciones Públicas Mass 39 0.003923
Fundaciones Mass 22 0.002213
Empresas y Colectivos Estratégicas 21 0.002113
Otros 121 3 0.000302
Fundaciones Mid, Mid + 1 0.000101
msf_program__c: programa al que pertenece.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_programaherencias__c: Variable booleana
In [727]:
# Vamos a realizar analisis por cada variable
var = "msf_programaherencias__c"
In [728]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_programaherencias__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_programaherencias__c es 0. Lo que supone un 0.0%
In [729]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[729]:
# Tot % Tot
False 988546 99.444905
True 5518 0.555095
msf_programaherencias__c: indicador de algun tipo de relacion con el programa de herencias.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_programais__c: Variable booleana
In [730]:
# Vamos a realizar analisis por cada variable
var = "msf_programais__c"
In [731]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_programais__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_programais__c es 0. Lo que supone un 0.0%
In [732]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[732]:
# Tot % Tot
False 993788 99.972235
True 276 0.027765
msf_programais__c: indicador de promotor en iniciativa solidaria.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_pressurecomplaint__c: Variable booleana
In [733]:
# Vamos a realizar analisis por cada variable
var = "msf_pressurecomplaint__c"
In [734]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_pressurecomplaint__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_pressurecomplaint__c es 0. Lo que supone un 0.0%
In [735]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[735]:
# Tot % Tot
False 988990 99.48957
True 5074 0.51043
msf_pressurecomplaint__c: queja por presión telemarketing.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_recencydonorcont__c: Variable numerica
In [736]:
# Vamos a realizar analisis por cada variable
var = "msf_recencydonorcont__c"
In [737]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_recencydonorcont__c es 727424. Lo que supone un 73.17677735035168%
El nº de vacios para la variable msf_recencydonorcont__c es 0. Lo que supone un 0.0%
Out[737]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_recencydonorcont__c']
In [738]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[738]:
# Tot % Tot
218.0 6535 2.450870
1102.0 6475 2.428368
128.0 5796 2.173717
1132.0 3950 1.481398
583.0 3579 1.342259
... ... ...
4801.0 1 0.000375
11117.0 1 0.000375
4547.0 1 0.000375
5426.0 1 0.000375
4983.0 1 0.000375

8427 rows × 2 columns

msf_recencydonorcont__c: numero de dias desde el ultimo donativo.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_recencyrecurringdonorcont__c: Variable numerica
In [739]:
# Vamos a realizar analisis por cada variable
var = "msf_recencyrecurringdonorcont__c"
In [740]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_recencyrecurringdonorcont__c es 59274. Lo que supone un 5.962795152022405%
El nº de vacios para la variable msf_recencyrecurringdonorcont__c es 0. Lo que supone un 0.0%
In [741]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[741]:
# Tot % Tot
4.0 391646 41.896683
36.0 20951 2.241252
66.0 20310 2.172680
186.0 13427 1.436365
156.0 13005 1.391222
128.0 10337 1.105810
218.0 10233 1.094684
95.0 8499 0.909188
247.0 7675 0.821040
340.0 7139 0.763701
277.0 6996 0.748403
309.0 6101 0.652660
1983.0 5297 0.566651
2012.0 4219 0.451331
1648.0 3902 0.417420
2042.0 3888 0.415922
1314.0 3803 0.406829
1678.0 3790 0.405439
583.0 3755 0.401694
948.0 3667 0.392281
1955.0 3615 0.386718
1283.0 3555 0.380299
1769.0 3360 0.359439
1251.0 3341 0.357406
550.0 3333 0.356551
1740.0 3308 0.353876
1832.0 3256 0.348314
1922.0 3252 0.347886
914.0 3250 0.347672
1375.0 3210 0.343393
401.0 3201 0.342430
1405.0 3195 0.341788
1223.0 3187 0.340932
1618.0 3179 0.340076
1437.0 3175 0.339648
612.0 3165 0.338579
1802.0 3134 0.335262
858.0 3133 0.335155
2105.0 3127 0.334514
1863.0 3100 0.331625
1009.0 3095 0.331090
644.0 3092 0.330769
1468.0 3088 0.330342
766.0 3082 0.329700
368.0 3053 0.326597
2074.0 3053 0.326597
1709.0 3050 0.326276
886.0 3010 0.321997
1590.0 2989 0.319751
674.0 2986 0.319430
2378.0 2958 0.316435
1040.0 2954 0.316007
2410.0 2924 0.312798
493.0 2910 0.311300
1892.0 2902 0.310444
976.0 2890 0.309160
2136.0 2887 0.308839
1342.0 2887 0.308839
521.0 2851 0.304988
1559.0 2850 0.304881
827.0 2806 0.300174
431.0 2800 0.299533
462.0 2772 0.296537
795.0 2755 0.294719
736.0 2754 0.294612
1102.0 2751 0.294291
1528.0 2680 0.286695
704.0 2673 0.285947
2167.0 2618 0.280063
1496.0 2591 0.277175
2196.0 2528 0.270435
1069.0 2523 0.269900
1192.0 2511 0.268616
1131.0 2437 0.260700
2775.0 2431 0.260058
2469.0 2410 0.257812
2347.0 2408 0.257598
2319.0 2404 0.257170
2258.0 2376 0.254175
2228.0 2346 0.250965
1161.0 2302 0.246259
2287.0 2289 0.244868
2714.0 2283 0.244226
2439.0 2275 0.243370
3869.0 2256 0.241338
2742.0 2228 0.238342
2501.0 2219 0.237380
3109.0 2142 0.229142
3474.0 2139 0.228821
4205.0 2138 0.228714
3140.0 2119 0.226682
2532.0 2103 0.224970
2837.0 2095 0.224115
2563.0 2091 0.223687
3839.0 2055 0.219835
2654.0 2041 0.218338
2685.0 2040 0.218231
3932.0 2028 0.216947
2867.0 2026 0.216733
4175.0 2021 0.216198
2593.0 2016 0.215663
4237.0 2013 0.215342
3078.0 1989 0.212775
3809.0 1982 0.212026
3050.0 1980 0.211812
2804.0 1977 0.211491
3020.0 1970 0.210743
3442.0 1966 0.210315
2623.0 1932 0.206677
3505.0 1912 0.204538
2929.0 1903 0.203575
2958.0 1889 0.202077
3781.0 1834 0.196194
2896.0 1822 0.194910
4114.0 1812 0.193840
4146.0 1811 0.193733
3566.0 1791 0.191594
4083.0 1788 0.191273
3900.0 1780 0.190417
3414.0 1780 0.190417
3201.0 1767 0.189026
3960.0 1761 0.188385
4023.0 1753 0.187529
2987.0 1736 0.185710
3749.0 1710 0.182929
3596.0 1670 0.178650
3230.0 1660 0.177580
3993.0 1634 0.174799
3384.0 1633 0.174692
3351.0 1630 0.174371
3263.0 1628 0.174157
3293.0 1607 0.171910
3169.0 1603 0.171482
3659.0 1585 0.169557
4601.0 1539 0.164636
4569.0 1539 0.164636
4512.0 1533 0.163994
3719.0 1526 0.163245
4051.0 1517 0.162282
4266.0 1482 0.158538
3320.0 1475 0.157789
4295.0 1435 0.153510
4327.0 1429 0.152869
4540.0 1424 0.152334
3533.0 1422 0.152120
4390.0 1389 0.148590
4420.0 1369 0.146450
3627.0 1365 0.146022
4481.0 1352 0.144631
3687.0 1345 0.143883
4450.0 1275 0.136394
5268.0 1271 0.135966
5300.0 1247 0.133399
4358.0 1239 0.132543
4660.0 1216 0.130083
5332.0 1207 0.129120
5392.0 1188 0.127087
4692.0 1180 0.126232
4966.0 1179 0.126125
4631.0 1175 0.125697
5423.0 1155 0.123557
4723.0 1097 0.117353
4755.0 1082 0.115748
4814.0 1067 0.114143
5665.0 1066 0.114036
5605.0 1043 0.111576
4784.0 1041 0.111362
4846.0 1035 0.110720
5240.0 1033 0.110506
5633.0 1029 0.110078
4905.0 1028 0.109971
4877.0 1025 0.109650
5210.0 1024 0.109543
5027.0 1023 0.109436
4933.0 1022 0.109329
5697.0 985 0.105371
4996.0 967 0.103446
5482.0 947 0.101306
5360.0 945 0.101092
5057.0 940 0.100557
5545.0 927 0.099167
5573.0 895 0.095743
5178.0 895 0.095743
5514.0 867 0.092748
5941.0 863 0.092320
5147.0 847 0.090609
5119.0 845 0.090395
5756.0 842 0.090074
5727.0 836 0.089432
5848.0 809 0.086544
5787.0 806 0.086223
6029.0 772 0.082585
5972.0 769 0.082264
5087.0 760 0.081302
5447.0 750 0.080232
5997.0 717 0.076702
5909.0 702 0.075097
5819.0 700 0.074883
5877.0 646 0.069106
7738.0 643 0.068786
6123.0 628 0.067181
6364.0 603 0.064506
6393.0 597 0.063865
6062.0 571 0.061083
6151.0 542 0.057981
6426.0 532 0.056911
6336.0 521 0.055734
6245.0 521 0.055734
6305.0 520 0.055627
6214.0 519 0.055520
6091.0 518 0.055414
6274.0 508 0.054344
6184.0 495 0.052953
6487.0 448 0.047925
6669.0 443 0.047390
6518.0 439 0.046962
6700.0 428 0.045786
6549.0 423 0.045251
6728.0 418 0.044716
6456.0 407 0.043539
6639.0 396 0.042362
6759.0 390 0.041721
6609.0 385 0.041186
6882.0 380 0.040651
6578.0 358 0.038297
7097.0 355 0.037976
7068.0 329 0.035195
7128.0 323 0.034553
6946.0 320 0.034232
6849.0 315 0.033697
7037.0 307 0.032842
7493.0 286 0.030595
6820.0 286 0.030595
7462.0 277 0.029632
6789.0 273 0.029204
8315.0 267 0.028563
7007.0 263 0.028135
7585.0 254 0.027172
7950.0 250 0.026744
8192.0 242 0.025888
7403.0 236 0.025246
6976.0 236 0.025246
7159.0 235 0.025139
7858.0 232 0.024818
7220.0 215 0.023000
7312.0 207 0.022144
7615.0 207 0.022144
7250.0 207 0.022144
6915.0 205 0.021930
7434.0 203 0.021716
7189.0 200 0.021395
8042.0 200 0.021395
9776.0 194 0.020753
7342.0 187 0.020004
8133.0 187 0.020004
7646.0 186 0.019898
8223.0 185 0.019791
7373.0 183 0.019577
8164.0 178 0.019042
7281.0 176 0.018828
7524.0 171 0.018293
7889.0 171 0.018293
7554.0 169 0.018079
7827.0 165 0.017651
9684.0 162 0.017330
9411.0 162 0.017330
8254.0 157 0.016795
8494.0 156 0.016688
8407.0 154 0.016474
9288.0 154 0.016474
7768.0 153 0.016367
7980.0 152 0.016260
7919.0 151 0.016153
9653.0 148 0.015832
8923.0 146 0.015618
10141.0 146 0.015618
8558.0 145 0.015512
7799.0 145 0.015512
9594.0 143 0.015298
9959.0 143 0.015298
8954.0 136 0.014549
9868.0 133 0.014228
8345.0 131 0.014014
10019.0 130 0.013907
8072.0 130 0.013907
9503.0 129 0.013800
8773.0 129 0.013800
8468.0 124 0.013265
8864.0 123 0.013158
9319.0 122 0.013051
8011.0 121 0.012944
9229.0 120 0.012837
9046.0 120 0.012837
8376.0 118 0.012623
8103.0 117 0.012516
8284.0 114 0.012195
2198.0 110 0.011767
8437.0 109 0.011660
9260.0 108 0.011553
10049.0 107 0.011446
8577.0 107 0.011446
8681.0 107 0.011446
10384.0 107 0.011446
7668.0 104 0.011125
8620.0 101 0.010805
8521.0 100 0.010698
10234.0 98 0.010484
9138.0 97 0.010377
10325.0 91 0.009735
8895.0 90 0.009628
8985.0 89 0.009521
9625.0 85 0.009093
10414.0 84 0.008986
7665.0 83 0.008879
8803.0 82 0.008772
2379.0 77 0.008237
8711.0 75 0.008023
9990.0 73 0.007809
2045.0 72 0.007702
8742.0 70 0.007488
9715.0 68 0.007274
8650.0 68 0.007274
2014.0 67 0.007167
2776.0 67 0.007167
2990.0 66 0.007060
2348.0 66 0.007060
7695.0 66 0.007060
3079.0 65 0.006953
2259.0 65 0.006953
2075.0 64 0.006846
2320.0 63 0.006739
2624.0 63 0.006739
2898.0 62 0.006633
2471.0 61 0.006526
2959.0 59 0.006312
10111.0 59 0.006312
9533.0 59 0.006312
9076.0 58 0.006205
2806.0 57 0.006098
10356.0 57 0.006098
9168.0 55 0.005884
3051.0 55 0.005884
9805.0 55 0.005884
3416.0 54 0.005777
9745.0 54 0.005777
2289.0 53 0.005670
2745.0 53 0.005670
1741.0 52 0.005563
3141.0 52 0.005563
10502.0 52 0.005563
3110.0 52 0.005563
8834.0 52 0.005563
10264.0 52 0.005563
2440.0 51 0.005456
1924.0 50 0.005349
3232.0 50 0.005349
9107.0 50 0.005349
2106.0 50 0.005349
3506.0 49 0.005242
1680.0 48 0.005135
3597.0 48 0.005135
1833.0 47 0.005028
9015.0 47 0.005028
10081.0 47 0.005028
3355.0 46 0.004921
9441.0 46 0.004921
1315.0 46 0.004921
4085.0 45 0.004814
9380.0 45 0.004814
5270.0 45 0.004814
10507.0 45 0.004814
4115.0 44 0.004707
1253.0 43 0.004600
9472.0 43 0.004600
9896.0 42 0.004493
1710.0 41 0.004386
9350.0 40 0.004279
9564.0 40 0.004279
10596.0 39 0.004172
1224.0 39 0.004172
3536.0 39 0.004172
10166.0 38 0.004065
4024.0 38 0.004065
9929.0 38 0.004065
10476.0 37 0.003958
9199.0 37 0.003958
3202.0 37 0.003958
3171.0 36 0.003851
10749.0 36 0.003851
3294.0 35 0.003744
1284.0 35 0.003744
5393.0 35 0.003744
3962.0 35 0.003744
9836.0 34 0.003637
3385.0 34 0.003637
1163.0 34 0.003637
10443.0 34 0.003637
1132.0 34 0.003637
3324.0 33 0.003530
3567.0 33 0.003530
4451.0 33 0.003530
1529.0 32 0.003423
10694.0 32 0.003423
1193.0 32 0.003423
1345.0 31 0.003316
4602.0 31 0.003316
10774.0 30 0.003209
1406.0 30 0.003209
5242.0 30 0.003209
1771.0 30 0.003209
3444.0 30 0.003209
3720.0 30 0.003209
1894.0 30 0.003209
7703.0 29 0.003102
1376.0 29 0.003102
4206.0 29 0.003102
3840.0 28 0.002995
4571.0 27 0.002888
3901.0 27 0.002888
10964.0 27 0.002888
10294.0 27 0.002888
1649.0 27 0.002888
5211.0 27 0.002888
5301.0 26 0.002781
5058.0 26 0.002781
5089.0 26 0.002781
7700.0 26 0.002781
11051.0 26 0.002781
4816.0 25 0.002674
4298.0 25 0.002674
4267.0 25 0.002674
4359.0 25 0.002674
3871.0 25 0.002674
10869.0 24 0.002567
5515.0 24 0.002567
3750.0 24 0.002567
5576.0 24 0.002567
5636.0 23 0.002460
3689.0 23 0.002460
1071.0 23 0.002460
10203.0 22 0.002353
10960.0 22 0.002353
5028.0 21 0.002246
4632.0 21 0.002246
3475.0 20 0.002140
1498.0 20 0.002140
5362.0 20 0.002140
5485.0 20 0.002140
4054.0 20 0.002140
4328.0 20 0.002140
3628.0 19 0.002033
5607.0 19 0.002033
5728.0 19 0.002033
11359.0 18 0.001926
10142.0 18 0.001926
4785.0 18 0.001926
6428.0 18 0.001926
883.0 18 0.001926
4693.0 18 0.001926
5150.0 18 0.001926
11206.0 17 0.001819
10743.0 17 0.001819
5820.0 17 0.001819
5454.0 17 0.001819
4724.0 17 0.001819
4936.0 17 0.001819
5181.0 17 0.001819
11086.0 16 0.001712
10834.0 16 0.001712
10537.0 16 0.001712
6307.0 16 0.001712
4967.0 16 0.001712
6093.0 16 0.001712
10718.0 15 0.001605
6216.0 15 0.001605
10599.0 15 0.001605
5546.0 15 0.001605
8589.0 14 0.001498
11139.0 14 0.001498
10415.0 14 0.001498
11176.0 14 0.001498
11145.0 14 0.001498
5973.0 14 0.001498
5759.0 13 0.001391
6550.0 13 0.001391
5120.0 13 0.001391
5667.0 13 0.001391
9898.0 13 0.001391
11025.0 13 0.001391
10721.0 13 0.001391
6124.0 12 0.001284
8529.0 12 0.001284
10050.0 12 0.001284
6277.0 12 0.001284
6154.0 11 0.001177
5942.0 11 0.001177
4663.0 11 0.001177
6246.0 11 0.001177
5881.0 10 0.001070
6032.0 10 0.001070
5789.0 10 0.001070
5698.0 10 0.001070
10929.0 10 0.001070
4997.0 10 0.001070
6458.0 9 0.000963
10841.0 9 0.000963
10780.0 9 0.000963
10624.0 9 0.000963
6519.0 9 0.000963
6001.0 8 0.000856
11329.0 8 0.000856
10568.0 8 0.000856
11098.0 8 0.000856
10805.0 8 0.000856
11419.0 7 0.000749
5851.0 7 0.000749
11224.0 7 0.000749
10887.0 7 0.000749
10652.0 7 0.000749
5912.0 7 0.000749
10811.0 6 0.000642
11163.0 6 0.000642
6489.0 6 0.000642
5.0 6 0.000642
11510.0 6 0.000642
6185.0 6 0.000642
6063.0 6 0.000642
10172.0 5 0.000535
9837.0 5 0.000535
10629.0 5 0.000535
11857.0 5 0.000535
10295.0 5 0.000535
10690.0 4 0.000428
11079.0 4 0.000428
11856.0 4 0.000428
11542.0 4 0.000428
11114.0 4 0.000428
10446.0 4 0.000428
7667.0 4 0.000428
10933.0 3 0.000321
11267.0 3 0.000321
11633.0 3 0.000321
11029.0 3 0.000321
6397.0 3 0.000321
11237.0 3 0.000321
9806.0 3 0.000321
11285.0 3 0.000321
11695.0 3 0.000321
37.0 3 0.000321
11826.0 3 0.000321
11298.0 2 0.000214
248.0 2 0.000214
11479.0 2 0.000214
11567.0 2 0.000214
11055.0 2 0.000214
11076.0 2 0.000214
10660.0 2 0.000214
463.0 2 0.000214
796.0 2 0.000214
11664.0 2 0.000214
11358.0 2 0.000214
11238.0 2 0.000214
7690.0 2 0.000214
11772.0 2 0.000214
11103.0 2 0.000214
10902.0 2 0.000214
11370.0 2 0.000214
11572.0 1 0.000107
8698.0 1 0.000107
11603.0 1 0.000107
9561.0 1 0.000107
11476.0 1 0.000107
11867.0 1 0.000107
11723.0 1 0.000107
10572.0 1 0.000107
11631.0 1 0.000107
767.0 1 0.000107
11888.0 1 0.000107
11968.0 1 0.000107
705.0 1 0.000107
11450.0 1 0.000107
11937.0 1 0.000107
11873.0 1 0.000107
8731.0 1 0.000107
11420.0 1 0.000107
1734.0 1 0.000107
11793.0 1 0.000107
10994.0 1 0.000107
11037.0 1 0.000107
11022.0 1 0.000107
219.0 1 0.000107
11053.0 1 0.000107
11026.0 1 0.000107
310.0 1 0.000107
67.0 1 0.000107
949.0 1 0.000107
7697.0 1 0.000107
198.0 1 0.000107
1006.0 1 0.000107
432.0 1 0.000107
msf_recencyrecurringdonorcont__c: numero de dias desde la ultima aportacion de socio recurrente.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_recencytotalcont__c: Variable numerica
In [742]:
# Vamos a realizar analisis por cada variable
var = "msf_recencytotalcont__c"
In [743]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_recencytotalcont__c es 57038. Lo que supone un 5.73785993658356%
El nº de vacios para la variable msf_recencytotalcont__c es 0. Lo que supone un 0.0%
In [744]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[744]:
# Tot % Tot
4.0 392601 41.898624
36.0 21155 2.257675
66.0 20464 2.183931
186.0 12876 1.374135
156.0 12486 1.332514
... ... ...
598.0 1 0.000107
9228.0 1 0.000107
4250.0 1 0.000107
4469.0 1 0.000107
896.0 1 0.000107

4156 rows × 2 columns

msf_recencytotalcont__c: numero de dias desde la ultima aportacion.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_scoringrfvdonor__c: Variable numerica
In [745]:
# Vamos a realizar analisis por cada variable
var = "msf_scoringrfvdonor__c"
In [746]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_scoringrfvdonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_scoringrfvdonor__c es 0. Lo que supone un 0.0%
In [747]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[747]:
# Tot % Tot
0.0 727424 73.176777
1.4 27938 2.810483
1.2 25594 2.574683
1.0 22498 2.263235
1.8 22083 2.221487
1.5 19069 1.918287
1.6 15558 1.565090
1.7 14356 1.444173
2.3 11514 1.158276
1.9 10863 1.092787
2.0 8179 0.822784
2.1 8038 0.808600
2.2 7516 0.756088
2.8 6967 0.700860
2.5 5408 0.544029
2.4 5165 0.519584
3.0 5128 0.515862
2.6 4798 0.482665
3.3 4340 0.436592
3.2 3851 0.387400
3.8 3612 0.363357
2.7 3510 0.353096
3.6 3167 0.318591
3.5 3047 0.306519
4.1 2964 0.298170
2.9 2424 0.243847
3.4 2418 0.243244
3.1 2191 0.220408
3.9 2143 0.215580
3.7 2011 0.202301
4.4 1451 0.145966
4.0 1155 0.116190
4.2 1111 0.111763
4.3 1027 0.103313
1.3 967 0.097277
4.6 787 0.079170
4.7 592 0.059554
4.9 498 0.050097
4.5 492 0.049494
4.8 473 0.047582
5.0 366 0.036819
5.1 339 0.034102
5.2 221 0.022232
5.4 162 0.016297
5.5 133 0.013379
5.3 93 0.009356
6.0 82 0.008249
5.7 74 0.007444
5.6 73 0.007344
5.9 45 0.004527
5.8 32 0.003219
6.5 26 0.002616
0.8 16 0.001610
6.2 12 0.001207
0.4 11 0.001107
0.6 10 0.001006
6.1 9 0.000905
0.5 6 0.000604
6.3 4 0.000402
6.7 4 0.000402
6.6 3 0.000302
6.4 3 0.000302
0.2 3 0.000302
0.7 3 0.000302
1.1 2 0.000201
7.0 2 0.000201
6.8 2 0.000201
0.9 1 0.000101
msf_scoringrfvdonor__c: scoring donante.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_scoringrfvrecurringdonor__c: Variable numerica
In [748]:
# Vamos a realizar analisis por cada variable
var = "msf_scoringrfvrecurringdonor__c"
In [749]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_scoringrfvrecurringdonor__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_scoringrfvrecurringdonor__c es 0. Lo que supone un 0.0%
In [750]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[750]:
# Tot % Tot
5.0 131141 13.192410
4.5 97173 9.775326
3.5 76494 7.695078
0.0 58415 5.876382
0.4 57835 5.818036
0.2 43619 4.387947
3.0 38256 3.848444
0.6 32846 3.304214
1.9 29234 2.940857
2.1 28489 2.865912
1.7 27945 2.811187
1.0 27282 2.744491
0.8 25872 2.602649
0.5 21279 2.140607
0.7 21045 2.117067
2.0 20994 2.111936
4.7 20535 2.065762
4.2 18389 1.849881
1.4 18145 1.825335
1.5 15185 1.527568
1.8 14527 1.461375
0.9 14394 1.447995
2.5 13841 1.392365
1.6 13796 1.387838
3.2 13232 1.331101
1.1 13128 1.320639
2.3 11952 1.202337
5.5 11441 1.150932
4.0 11273 1.134032
1.2 11248 1.131517
1.3 9431 0.948732
4.4 9403 0.945915
3.9 8215 0.826406
2.9 5830 0.586481
2.7 5153 0.518377
2.2 4636 0.466368
2.4 2281 0.229462
6.0 2103 0.211556
3.7 1697 0.170713
3.6 1357 0.136510
4.1 1343 0.135102
2.6 1061 0.106734
3.4 753 0.075750
5.2 752 0.075649
4.9 371 0.037322
3.1 146 0.014687
5.7 143 0.014385
6.5 92 0.009255
4.6 75 0.007545
5.4 67 0.006740
2.8 45 0.004527
3.3 31 0.003119
4.3 25 0.002515
3.8 18 0.001811
5.1 12 0.001207
6.2 6 0.000604
4.8 4 0.000402
5.9 3 0.000302
7.0 2 0.000201
5.6 2 0.000201
6.1 1 0.000101
6.7 1 0.000101
msf_scoringrfvrecurringdonor__c: scoring donante recurrente.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_scoringrvtotal__c: Variable numerica
In [751]:
# Vamos a realizar analisis por cada variable
var = "msf_scoringrvtotal__c"
In [752]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_scoringrvtotal__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_scoringrvtotal__c es 0. Lo que supone un 0.0%
In [753]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[753]:
# Tot % Tot
5.0 193805 19.496230
4.2 170212 17.122841
2.6 105584 10.621449
1.8 75919 7.637235
0.0 56220 5.655571
3.4 51904 5.221394
1.2 36141 3.635681
2.0 34384 3.458932
3.6 31987 3.217801
1.0 28277 2.844585
1.4 26823 2.698317
3.8 26673 2.683228
4.6 25861 2.601543
4.4 25238 2.538871
2.2 25228 2.537865
5.8 18119 1.822720
1.6 11432 1.150027
4.8 9145 0.919961
4.0 8318 0.836767
2.4 7984 0.803168
2.8 6860 0.690096
3.0 5502 0.553485
6.6 4070 0.409430
3.2 2261 0.227450
5.2 1890 0.190129
5.4 1787 0.179767
5.6 763 0.076756
6.0 487 0.048991
6.2 431 0.043357
7.4 343 0.034505
6.4 153 0.015391
0.8 74 0.007444
8.2 43 0.004326
6.8 33 0.003320
7.0 28 0.002817
0.6 26 0.002616
0.4 21 0.002113
7.2 13 0.001308
0.2 10 0.001006
7.8 8 0.000805
7.6 4 0.000402
8.0 3 0.000302
msf_scoringrvtotal__c: scoring total.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_mailingsegment__c: Variable categorica
In [754]:
# Vamos a realizar analisis por cada variable
var = "msf_mailingsegment__c"
In [755]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_mailingsegment__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_mailingsegment__c es 7. Lo que supone un 0.0007041800125545236%
In [756]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[756]:
# Tot % Tot
SOC NO REC SIN EXTRA 313671 31.554407
BAJAS ANTIGUAS 154299 15.522039
BAJAS MUY ANTIGUAS 134593 13.539671
BAJAS NO REC 126262 12.701597
BAJAS ACT 50271 5.057119
SOC CON EXTRA ACT 47970 4.825645
BAJAS REC 41715 4.196410
SOC CON EXTRA NO REC 38684 3.891500
SOC NUEVOS 29424 2.959970
SOC CON EXTRA REC 28789 2.896091
SOC REC SIN EXTRA 21233 2.135979
EMPRESAS NO SOCIAS 3935 0.395850
EMPRESAS SOCIAS 2381 0.239522
No se está calculando la cadencia de donante 513 0.051606
DON MUY ANTIGUOS 155 0.015593
No cumple ninguno de los criterios anteriores 68 0.006841
DON ANTIGUOS 29 0.002917
DON OCA REC 16 0.001610
DON OCA ACT 13 0.001308
DON UNICO NO REC 12 0.001207
DON OCA NO REC 12 0.001207
7 0.000704
DON UNICO REC 4 0.000402
DON 1R AÑO 3 0.000302
DON PS ACT 3 0.000302
DON PS REC 2 0.000201
msf_mailingsegment__c: segmento colaborador.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_membertype__c: Variable categorica
In [757]:
# Vamos a realizar analisis por cada variable
var = "msf_membertype__c"
In [758]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_membertype__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_membertype__c es 0. Lo que supone un 0.0%
In [759]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[759]:
# Tot % Tot
Baja 425726 42.826820
Socio 301335 30.313441
Socio + Exdonante 132714 13.350649
Baja + Exdonante 79431 7.990532
Socio + Donante 48175 4.846267
Baja + Donante 5923 0.595837
Nada 503 0.050600
Exdonante 229 0.023037
Donante 18 0.001811
Nada (Donante SMS) 10 0.001006
msf_membertype__c: tipo de miembro.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npo02__totaloppamount__c: Variable numerica
In [760]:
# Vamos a realizar analisis por cada variable
var = "npo02__totaloppamount__c"
In [761]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__totaloppamount__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__totaloppamount__c es 0. Lo que supone un 0.0%
In [762]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[762]:
# Tot % Tot
0.00 57038 5.737860
30.00 12590 1.266518
60.00 12133 1.220545
10.00 11459 1.152743
20.00 11239 1.130611
... ... ...
6708.69 1 0.000101
25246.64 1 0.000101
554.89 1 0.000101
4367.70 1 0.000101
1628.70 1 0.000101

84077 rows × 2 columns

npo02__totaloppamount__c: total donado.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npo02__oppamountthisyear__c: Variable numerica
In [763]:
# Vamos a realizar analisis por cada variable
var = "npo02__oppamountthisyear__c"
In [764]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__oppamountthisyear__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__oppamountthisyear__c es 0. Lo que supone un 0.0%
In [765]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[765]:
# Tot % Tot
0.0 994064 100.0
npo02__OppAmountThisYear__c: importe total de aportaciones al año que realizó este año.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npo02__oppamount2yearsago__c: Variable numerica
In [766]:
# Vamos a realizar analisis por cada variable
var = "npo02__oppamount2yearsago__c"
In [767]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__oppamount2yearsago__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__oppamount2yearsago__c es 0. Lo que supone un 0.0%
In [768]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[768]:
# Tot % Tot
0.0 994064 100.0
npo02__oppamount2yearsago__c: importe total de aportaciones al año que realizó hace 2 años.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npo02__oppamountlastyear__c: Variable numerica
In [769]:
# Vamos a realizar analisis por cada variable
var = "npo02__oppamountlastyear__c"
In [770]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__oppamountlastyear__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable npo02__oppamountlastyear__c es 0. Lo que supone un 0.0%
In [771]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[771]:
# Tot % Tot
0.0 994064 100.0
npo02__oppamountlastyear__c: importe total de aportaciones al año que realizó el año pasado.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> npo02__best_gift_year_total__c: Variable numerica
In [772]:
# Vamos a realizar analisis por cada variable
var = "npo02__best_gift_year_total__c"
In [773]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__best_gift_year_total__c es 57038. Lo que supone un 5.73785993658356%
El nº de vacios para la variable npo02__best_gift_year_total__c es 0. Lo que supone un 0.0%
In [774]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[774]:
# Tot % Tot
120.00 114919 12.264227
180.00 57679 6.155539
60.00 55023 5.872089
240.00 38835 4.144495
144.00 26031 2.778045
... ... ...
3930.00 1 0.000107
857.00 1 0.000107
312.02 1 0.000107
790.40 1 0.000107
161.01 1 0.000107

7035 rows × 2 columns

npo02__best_gift_year_total__c: importe total de aportaciones al año que más ha aportado.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_totalfiscaloppamount__c: Variable numerica
In [775]:
# Vamos a realizar analisis por cada variable
var = "msf_totalfiscaloppamount__c"
In [776]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_totalfiscaloppamount__c es 3. Lo que supone un 0.0003017914339519387%
El nº de vacios para la variable msf_totalfiscaloppamount__c es 0. Lo que supone un 0.0%
In [777]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[777]:
# Tot % Tot
0.00 56551 5.688886
30.00 12607 1.268232
60.00 12131 1.220348
10.00 11532 1.160090
20.00 11273 1.134035
... ... ...
7055.63 1 0.000101
4528.35 1 0.000101
1450.25 1 0.000101
5346.25 1 0.000101
1628.70 1 0.000101

84207 rows × 2 columns

msf_totalfiscaloppamount__c: importe total de aportaciones fiscal cobradas.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_lastannualizedquota__c: Variable numerica
In [778]:
# Vamos a realizar analisis por cada variable
var = "msf_lastannualizedquota__c"
In [779]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lastannualizedquota__c es 41302. Lo que supone un 4.154863268360991%
El nº de vacios para la variable msf_lastannualizedquota__c es 0. Lo que supone un 0.0%
In [780]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[780]:
# Tot % Tot
1.200000e+02 205240 21.541581
1.800000e+02 103733 10.887609
6.000000e+01 96713 10.150804
2.400000e+02 66814 7.012664
1.440000e+02 51175 5.371226
7.200000e+01 35993 3.777754
3.600000e+02 25096 2.634026
3.000000e+02 24793 2.602224
3.600000e+01 22838 2.397031
9.600000e+01 20117 2.111440
8.400000e+01 16966 1.780718
1.680000e+02 16565 1.738629
1.000000e+02 14049 1.474555
7.212000e+01 11666 1.224440
5.000000e+01 9134 0.958686
2.040000e+02 8534 0.895712
4.000000e+01 8400 0.881647
8.000000e+01 7842 0.823081
6.000000e+02 7823 0.821086
2.000000e+02 7589 0.796526
2.000000e+01 7526 0.789914
4.800000e+02 6982 0.732817
2.160000e+02 6854 0.719382
3.000000e+01 6570 0.689574
1.320000e+02 6168 0.647381
1.560000e+02 6143 0.644757
1.500000e+02 5878 0.616943
1.080000e+02 5724 0.600780
1.920000e+02 5646 0.592593
4.800000e+01 5377 0.564359
4.200000e+02 5289 0.555123
3.120000e+02 4814 0.505268
2.640000e+02 4417 0.463600
1.200000e+01 4115 0.431902
5.196000e+01 3794 0.398211
6.010000e+01 3638 0.381837
1.202000e+02 3425 0.359481
2.280000e+02 3416 0.358537
1.000000e+01 3402 0.357067
1.600000e+02 3319 0.348356
7.200000e+02 3190 0.334816
3.005000e+01 3140 0.329568
2.760000e+02 2810 0.294932
9.000000e+01 2692 0.282547
1.442400e+02 2618 0.274780
1.500000e+01 2532 0.265754
1.400000e+02 2396 0.251479
2.163600e+02 2215 0.232482
7.000000e+01 2195 0.230383
3.606000e+02 2183 0.229123
4.000000e+02 1865 0.195747
3.840000e+02 1834 0.192493
1.200000e+03 1833 0.192388
2.500000e+01 1709 0.179373
5.400000e+02 1644 0.172551
2.880000e+02 1527 0.160271
7.500000e+01 1462 0.153449
2.400000e+01 1415 0.148516
2.520000e+02 1318 0.138335
3.240000e+02 1265 0.132772
2.500000e+02 1243 0.130463
3.360000e+02 1227 0.128783
3.000000e+00 1158 0.121541
1.803000e+01 1102 0.115664
2.600000e+02 1070 0.112305
9.015000e+01 1023 0.107372
2.404000e+02 1008 0.105798
3.960000e+02 827 0.086800
5.000000e+00 812 0.085226
1.300000e+02 770 0.080818
5.000000e+02 757 0.079453
1.100000e+02 752 0.078928
2.800000e+02 737 0.077354
2.200000e+02 676 0.070952
1.250000e+02 657 0.068957
3.500000e+01 646 0.067803
8.400000e+02 641 0.067278
6.600000e+02 641 0.067278
3.200000e+02 604 0.063395
4.500000e+01 599 0.062870
1.800000e+01 516 0.054158
4.808000e+01 492 0.051639
7.212000e+02 487 0.051115
0.000000e+00 465 0.048805
6.500000e+01 458 0.048071
4.080000e+02 449 0.047126
9.000000e+02 445 0.046706
8.800000e+01 444 0.046601
4.320000e+02 443 0.046496
9.600000e+02 436 0.045762
1.700000e+02 431 0.045237
3.200000e+01 425 0.044607
4.200000e+01 397 0.041668
1.502500e+02 395 0.041458
2.800000e+01 385 0.040409
2.100000e+02 373 0.039149
1.000000e+03 367 0.038520
5.500000e+01 366 0.038415
7.800000e+02 361 0.037890
5.200000e+01 359 0.037680
5.600000e+01 341 0.035791
4.440000e+02 340 0.035686
3.500000e+02 339 0.035581
2.404000e+01 336 0.035266
3.720000e+02 328 0.034426
6.240000e+02 324 0.034006
5.040000e+02 319 0.033482
1.750000e+02 316 0.033167
8.000000e+02 294 0.030858
3.606000e+01 289 0.030333
1.080000e+03 282 0.029598
8.500000e+01 274 0.028758
1.650000e+02 274 0.028758
1.120000e+02 268 0.028129
1.081200e+02 257 0.026974
2.200000e+01 253 0.026554
2.300000e+02 245 0.025715
6.000000e+00 244 0.025610
3.480000e+02 241 0.025295
1.800000e+03 239 0.025085
4.560000e+02 236 0.024770
5.200000e+02 231 0.024245
9.200000e+01 231 0.024245
1.040400e+02 195 0.020467
1.802400e+02 192 0.020152
2.884800e+02 192 0.020152
1.050000e+02 189 0.019837
6.800000e+01 189 0.019837
1.040000e+02 174 0.018263
1.520000e+02 170 0.017843
6.400000e+01 170 0.017843
1.600000e+01 167 0.017528
1.400000e+01 165 0.017318
3.485000e+01 165 0.017318
2.400000e+03 163 0.017108
1.280000e+02 162 0.017003
9.616000e+01 162 0.017003
5.160000e+02 157 0.016478
3.400000e+02 156 0.016373
4.400000e+02 155 0.016268
1.803000e+02 152 0.015954
1.350000e+02 151 0.015849
1.202000e+01 145 0.015219
1.730400e+02 142 0.014904
3.486000e+01 142 0.014904
1.240000e+02 141 0.014799
1.900000e+02 140 0.014694
5.280000e+02 140 0.014694
5.400000e+01 140 0.014694
5.520000e+02 139 0.014589
2.240000e+02 138 0.014484
1.394000e+02 137 0.014379
8.640000e+02 135 0.014169
1.440000e+03 135 0.014169
1.150000e+02 134 0.014064
6.200000e+01 134 0.014064
2.250000e+02 133 0.013959
6.010000e+00 130 0.013645
1.700000e+01 124 0.013015
4.400000e+01 124 0.013015
4.500000e+02 124 0.013015
1.039200e+02 122 0.012805
1.500000e+03 116 0.012175
1.480000e+02 112 0.011755
1.394400e+02 111 0.011650
7.224000e+01 110 0.011545
2.700000e+02 110 0.011545
7.000000e+02 110 0.011545
9.500000e+01 109 0.011440
8.000000e+00 109 0.011440
3.005000e+02 108 0.011335
2.320000e+02 108 0.011335
6.600000e+01 102 0.010706
1.082400e+02 102 0.010706
3.800000e+01 101 0.010601
4.920000e+02 98 0.010286
4.680000e+02 97 0.010181
3.612000e+01 94 0.009866
2.100000e+01 92 0.009656
7.600000e+01 91 0.009551
3.800000e+02 90 0.009446
1.020000e+03 89 0.009341
4.600000e+02 87 0.009131
1.360000e+02 82 0.008607
1.550000e+02 81 0.008502
4.327200e+02 80 0.008397
1.840000e+02 80 0.008397
5.760000e+02 77 0.008082
1.640000e+02 76 0.007977
3.604000e+01 75 0.007872
1.803600e+02 73 0.007662
3.100000e+02 73 0.007662
3.300000e+02 71 0.007452
3.900000e+02 67 0.007032
5.768000e+01 67 0.007032
1.081800e+03 66 0.006927
2.000000e+03 66 0.006927
7.400000e+01 65 0.006822
2.600000e+01 64 0.006717
9.315000e+01 64 0.006717
4.207000e+01 63 0.006612
2.750000e+02 63 0.006612
1.320000e+03 62 0.006507
3.300000e+01 59 0.006193
5.600000e+02 58 0.006088
1.923200e+02 57 0.005983
1.020000e+02 57 0.005983
4.808000e+02 56 0.005878
7.800000e+01 56 0.005878
6.010000e+02 55 0.005773
1.260000e+02 55 0.005773
2.080000e+02 55 0.005773
6.300000e+01 54 0.005668
2.480000e+02 54 0.005668
6.400000e+02 52 0.005458
4.183200e+02 52 0.005458
3.600000e+03 51 0.005353
1.450000e+02 51 0.005353
5.500000e+02 51 0.005353
2.700000e+01 51 0.005353
6.396000e+01 51 0.005353
6.010100e+02 51 0.005353
2.120000e+02 49 0.005143
3.400000e+01 49 0.005143
3.000000e+03 48 0.005038
7.440000e+02 48 0.005038
6.480000e+02 47 0.004933
1.682800e+02 47 0.004933
5.640000e+02 47 0.004933
1.160000e+02 46 0.004828
1.850000e+02 45 0.004723
6.200000e+02 44 0.004618
9.316000e+01 44 0.004618
7.596000e+01 44 0.004618
5.769600e+02 44 0.004618
8.414000e+01 43 0.004513
1.720000e+02 43 0.004513
6.008000e+01 42 0.004408
5.048400e+02 41 0.004303
7.000000e+00 41 0.004303
8.200000e+01 40 0.004198
1.300000e+01 39 0.004093
3.640000e+02 39 0.004093
3.700000e+01 38 0.003988
2.900000e+02 38 0.003988
8.654400e+02 37 0.003883
1.620000e+02 37 0.003883
1.960000e+02 37 0.003883
6.360000e+02 36 0.003778
5.409000e+01 35 0.003674
7.920000e+02 35 0.003674
1.880000e+02 35 0.003674
6.120000e+02 35 0.003674
3.250000e+02 34 0.003569
3.462000e+02 33 0.003464
4.000000e+00 33 0.003464
3.750000e+02 33 0.003464
6.500000e+02 32 0.003359
3.005100e+02 31 0.003254
4.182000e+02 31 0.003254
1.560000e+03 31 0.003254
1.081800e+02 31 0.003254
1.760000e+02 31 0.003254
3.900000e+01 31 0.003254
1.442400e+03 30 0.003149
1.802800e+02 30 0.003149
4.600000e+01 29 0.003044
1.008000e+03 29 0.003044
8.412000e+01 28 0.002939
9.000000e+00 27 0.002834
2.720000e+02 27 0.002834
3.700000e+02 27 0.002834
9.800000e+01 27 0.002834
1.154000e+02 25 0.002624
1.100000e+01 25 0.002624
2.360000e+02 25 0.002624
3.650000e+02 25 0.002624
3.608000e+01 24 0.002519
6.000000e+03 24 0.002519
6.700000e+01 24 0.002519
9.400000e+01 24 0.002519
1.740000e+02 23 0.002414
1.442000e+01 23 0.002414
2.885000e+01 23 0.002414
3.920000e+02 22 0.002309
2.350000e+02 22 0.002309
2.884000e+01 22 0.002309
3.614400e+02 22 0.002309
7.700000e+01 21 0.002204
2.150000e+02 21 0.002204
3.100000e+01 20 0.002099
9.360000e+02 20 0.002099
1.600000e+03 20 0.002099
7.813000e+01 20 0.002099
2.300000e+01 19 0.001994
7.500000e+02 19 0.001994
1.140000e+03 19 0.001994
1.803000e+03 19 0.001994
7.600000e+02 19 0.001994
8.600000e+01 19 0.001994
2.523600e+02 19 0.001994
6.100000e+01 19 0.001994
8.460000e+01 18 0.001889
6.720000e+02 18 0.001889
6.800000e+02 18 0.001889
1.153600e+02 17 0.001784
2.440000e+02 17 0.001784
5.100000e+01 17 0.001784
2.050000e+02 16 0.001679
1.140000e+02 16 0.001679
7.560000e+02 16 0.001679
8.700000e+01 16 0.001679
4.250000e+02 16 0.001679
6.840000e+02 15 0.001574
6.012000e+01 15 0.001574
1.152000e+03 15 0.001574
3.040000e+02 15 0.001574
1.680000e+03 15 0.001574
9.012000e+01 15 0.001574
1.220000e+02 15 0.001574
1.260000e+03 15 0.001574
5.300000e+01 14 0.001469
5.770000e+01 14 0.001469
1.980000e+02 14 0.001469
6.960000e+02 14 0.001469
5.769000e+01 14 0.001469
1.380000e+02 14 0.001469
2.103500e+02 14 0.001469
7.300000e+01 14 0.001469
8.652000e+01 14 0.001469
6.024000e+01 13 0.001364
7.320000e+02 13 0.001364
4.100000e+02 13 0.001364
1.060000e+02 13 0.001364
2.920000e+02 13 0.001364
1.950000e+02 13 0.001364
5.700000e+01 13 0.001364
1.204800e+02 13 0.001364
1.032000e+03 12 0.001259
1.201200e+02 12 0.001259
3.726000e+02 12 0.001259
4.300000e+01 12 0.001259
1.200000e+04 12 0.001259
7.680000e+02 12 0.001259
5.800000e+02 12 0.001259
1.340000e+02 12 0.001259
2.680000e+02 12 0.001259
9.300000e+01 12 0.001259
1.540000e+02 11 0.001155
8.040000e+02 11 0.001155
5.800000e+01 11 0.001155
2.524800e+02 11 0.001155
4.100000e+01 11 0.001155
2.840000e+02 11 0.001155
3.440000e+02 11 0.001155
1.230000e+02 11 0.001155
1.400000e+03 11 0.001155
1.803200e+02 11 0.001155
1.094400e+02 11 0.001155
1.420000e+02 11 0.001155
2.160000e+03 10 0.001050
8.800000e+02 10 0.001050
3.460800e+02 10 0.001050
1.010000e+02 10 0.001050
2.550000e+02 10 0.001050
4.800000e+03 10 0.001050
4.700000e+01 10 0.001050
1.202000e+03 9 0.000945
9.996000e+01 9 0.000945
2.040000e+03 9 0.000945
1.100000e+03 9 0.000945
3.160000e+02 9 0.000945
1.081600e+02 9 0.000945
6.924000e+02 9 0.000945
1.719600e+02 9 0.000945
1.920000e+03 9 0.000945
1.900000e+01 9 0.000945
2.020000e+02 9 0.000945
4.700000e+02 9 0.000945
2.560000e+02 9 0.000945
1.380000e+03 9 0.000945
1.040000e+03 8 0.000840
8.300000e+01 8 0.000840
2.220000e+02 8 0.000840
1.824000e+02 8 0.000840
2.900000e+01 8 0.000840
8.100000e+01 8 0.000840
9.020000e+00 8 0.000840
6.611000e+01 8 0.000840
3.012000e+01 8 0.000840
3.320000e+02 8 0.000840
2.960000e+02 8 0.000840
3.280000e+02 8 0.000840
1.820000e+02 8 0.000840
8.416000e+01 7 0.000735
8.160000e+02 7 0.000735
9.612000e+01 7 0.000735
2.850000e+02 7 0.000735
1.180000e+02 7 0.000735
2.163600e+03 7 0.000735
4.300000e+02 7 0.000735
4.900000e+01 7 0.000735
2.160000e+01 7 0.000735
7.200000e+03 7 0.000735
5.880000e+02 7 0.000735
1.860000e+02 7 0.000735
9.200000e+02 7 0.000735
5.040000e+01 7 0.000735
3.760000e+02 7 0.000735
2.100000e+03 7 0.000735
8.200000e+02 7 0.000735
1.082000e+02 7 0.000735
5.052000e+01 6 0.000630
3.050000e+02 6 0.000630
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1.044000e+03 1 0.000105
2.425000e+02 1 0.000105
4.484000e+01 1 0.000105
6.588000e+01 1 0.000105
1.983600e+02 1 0.000105
5.900000e+02 1 0.000105
1.160000e+03 1 0.000105
5.010000e+02 1 0.000105
1.399200e+02 1 0.000105
4.280000e+02 1 0.000105
7.196000e+01 1 0.000105
5.988000e+01 1 0.000105
2.410000e+02 1 0.000105
1.908000e+03 1 0.000105
2.064000e+03 1 0.000105
3.360000e+03 1 0.000105
3.602400e+02 1 0.000105
2.520000e+01 1 0.000105
2.803600e+02 1 0.000105
1.442430e+03 1 0.000105
6.006000e+01 1 0.000105
2.193600e+02 1 0.000105
3.006000e+01 1 0.000105
2.705000e+01 1 0.000105
2.308000e+02 1 0.000105
1.730000e+01 1 0.000105
3.860000e+02 1 0.000105
8.428800e+02 1 0.000105
6.132000e+01 1 0.000105
2.132000e+01 1 0.000105
2.451600e+02 1 0.000105
5.493000e+01 1 0.000105
6.600000e+03 1 0.000105
2.352000e+03 1 0.000105
4.666400e+02 1 0.000105
2.210000e+02 1 0.000105
1.752500e+02 1 0.000105
4.332000e+01 1 0.000105
9.014400e+02 1 0.000105
4.006000e+01 1 0.000105
4.059600e+02 1 0.000105
2.196000e+02 1 0.000105
1.009600e+02 1 0.000105
2.524200e+03 1 0.000105
4.447200e+02 1 0.000105
2.401000e+02 1 0.000105
3.000100e+02 1 0.000105
6.480000e+01 1 0.000105
1.153200e+02 1 0.000105
5.556000e+01 1 0.000105
2.115600e+02 1 0.000105
7.500000e+00 1 0.000105
1.121200e+02 1 0.000105
2.530000e+02 1 0.000105
2.430000e+02 1 0.000105
1.200800e+02 1 0.000105
2.598000e+01 1 0.000105
3.820000e+02 1 0.000105
2.890000e+02 1 0.000105
1.500000e+04 1 0.000105
1.810000e+02 1 0.000105
1.620500e+02 1 0.000105
1.514400e+02 1 0.000105
1.586640e+03 1 0.000105
1.930000e+02 1 0.000105
4.620000e+03 1 0.000105
1.204000e+03 1 0.000105
4.760000e+02 1 0.000105
1.939200e+02 1 0.000105
2.420000e+02 1 0.000105
8.400000e+03 1 0.000105
msf_lastannualizedquota__c: importe anualizado de la ultima cuota de socio.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_valuetotalcont__c: Variable numerica
In [781]:
# Vamos a realizar analisis por cada variable
var = "msf_valuetotalcont__c"
In [782]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_valuetotalcont__c es 520. Lo que supone un 0.052310515218336046%
El nº de vacios para la variable msf_valuetotalcont__c es 0. Lo que supone un 0.0%
In [783]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[783]:
# Tot % Tot
120.0 112276 11.300556
60.0 61017 6.141349
180.0 59534 5.992085
0.0 55793 5.615554
240.0 41562 4.183207
... ... ...
3420.0 1 0.000101
849.0 1 0.000101
657.0 1 0.000101
2318.0 1 0.000101
8400.0 1 0.000101

1697 rows × 2 columns

msf_valuetotalcont__c: valor colaborador.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_valuedonorcont__c: Variable numerica
In [784]:
# Vamos a realizar analisis por cada variable
var = "msf_valuedonorcont__c"
In [785]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_valuedonorcont__c es 727868. Lo que supone un 73.22144248257658%
El nº de vacios para la variable msf_valuedonorcont__c es 0. Lo que supone un 0.0%
Out[785]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_recencydonorcont__c',
 'msf_valuedonorcont__c']
In [786]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[786]:
# Tot % Tot
30.00 28197 10.592571
60.00 25845 9.709011
100.00 24352 9.148146
50.00 24260 9.113585
20.00 24043 9.032067
... ... ...
5.92 1 0.000376
55.06 1 0.000376
1160.00 1 0.000376
2550.00 1 0.000376
1.17 1 0.000376

2389 rows × 2 columns

msf_valuedonorcont__c: suma de las donaciones de los ultimoss 365 dias.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_lastyeardonorvalue__c: Variable numerica
In [787]:
# Vamos a realizar analisis por cada variable
var = "msf_lastyeardonorvalue__c"
In [788]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lastyeardonorvalue__c es 939961. Lo que supone un 94.5573926829661%
El nº de vacios para la variable msf_lastyeardonorvalue__c es 0. Lo que supone un 0.0%
Out[788]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_recencydonorcont__c',
 'msf_valuedonorcont__c',
 'msf_lastyeardonorvalue__c']
In [789]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[789]:
# Tot % Tot
100.00 5575 10.304419
50.00 5537 10.234183
20.00 5037 9.310020
30.00 4499 8.315620
60.00 4144 7.659464
1.00 2591 4.789014
10.00 2575 4.759440
200.00 2036 3.763192
40.00 2007 3.709591
150.00 1375 2.541449
300.00 1097 2.027614
120.00 1035 1.913018
90.00 1012 1.870506
25.00 982 1.815056
15.00 907 1.676432
80.00 833 1.539656
5.00 819 1.513779
250.00 636 1.175536
500.00 617 1.140417
70.00 443 0.818809
2.00 431 0.796629
400.00 417 0.770752
125.00 412 0.761510
1000.00 301 0.556346
110.00 262 0.484262
600.00 259 0.478717
180.00 255 0.471323
45.00 250 0.462082
160.00 250 0.462082
140.00 194 0.358575
39.00 190 0.351182
75.00 185 0.341940
130.00 176 0.325305
3.00 171 0.316064
350.00 169 0.312367
66.00 164 0.303126
240.00 154 0.284642
450.00 131 0.242131
55.00 122 0.225496
35.00 115 0.212558
16.00 113 0.208861
220.00 105 0.194074
2000.00 100 0.184833
12.00 100 0.184833
170.00 96 0.177439
26.00 90 0.166349
78.00 90 0.166349
800.00 89 0.164501
190.00 88 0.162653
51.00 83 0.153411
61.00 82 0.151563
175.00 82 0.151563
4.00 82 0.151563
210.00 80 0.147866
6.00 78 0.144169
8.00 77 0.142321
270.00 77 0.142321
700.00 77 0.142321
550.00 68 0.125686
21.00 68 0.125686
1500.00 66 0.121990
65.00 64 0.118293
31.00 62 0.114596
260.00 60 0.110900
900.00 59 0.109051
750.00 58 0.107203
24.00 58 0.107203
225.00 57 0.105355
101.00 55 0.101658
3000.00 55 0.101658
156.00 52 0.096113
325.00 48 0.088720
360.00 47 0.086871
650.00 47 0.086871
7.00 45 0.083175
1200.00 44 0.081326
99.00 44 0.081326
230.00 41 0.075781
85.00 39 0.072085
280.00 39 0.072085
32.00 37 0.068388
320.00 36 0.066540
11.00 32 0.059146
69.00 30 0.055450
275.00 30 0.055450
375.00 28 0.051753
105.00 28 0.051753
1100.00 27 0.049905
36.00 27 0.049905
340.00 27 0.049905
41.00 27 0.049905
34.00 26 0.048056
18.00 26 0.048056
290.00 26 0.048056
185.00 26 0.048056
46.00 25 0.046208
1300.00 24 0.044360
850.00 24 0.044360
14.00 24 0.044360
205.00 23 0.042512
370.00 23 0.042512
91.00 23 0.042512
138.00 22 0.040663
1400.00 22 0.040663
245.00 22 0.040663
165.00 21 0.038815
420.00 21 0.038815
102.00 21 0.038815
52.00 20 0.036967
151.00 19 0.035118
89.00 19 0.035118
4000.00 19 0.035118
2500.00 19 0.035118
126.00 19 0.035118
330.00 19 0.035118
425.00 18 0.033270
62.00 18 0.033270
56.00 18 0.033270
310.00 18 0.033270
121.00 18 0.033270
115.00 18 0.033270
111.00 18 0.033270
5000.00 17 0.031422
9.00 17 0.031422
95.00 17 0.031422
390.00 17 0.031422
675.00 16 0.029573
81.00 16 0.029573
525.00 16 0.029573
117.00 16 0.029573
128.00 15 0.027725
76.00 15 0.027725
950.00 15 0.027725
48.00 15 0.027725
1600.00 15 0.027725
6000.00 14 0.025877
42.00 14 0.025877
1250.00 14 0.025877
178.00 14 0.025877
22.00 14 0.025877
86.00 13 0.024028
145.00 13 0.024028
10000.00 13 0.024028
72.00 13 0.024028
135.00 12 0.022180
1050.00 12 0.022180
295.00 12 0.022180
380.00 12 0.022180
480.00 12 0.022180
520.00 12 0.022180
96.00 12 0.022180
195.00 12 0.022180
27.00 11 0.020332
410.00 11 0.020332
475.00 11 0.020332
92.00 11 0.020332
273.00 11 0.020332
1450.00 11 0.020332
64.00 11 0.020332
139.00 10 0.018483
560.00 10 0.018483
158.00 10 0.018483
116.00 10 0.018483
625.00 10 0.018483
430.00 10 0.018483
28.00 10 0.018483
63.00 10 0.018483
1800.00 10 0.018483
306.00 10 0.018483
155.00 10 0.018483
440.00 9 0.016635
490.00 9 0.016635
148.00 9 0.016635
201.00 9 0.016635
53.00 9 0.016635
215.00 9 0.016635
198.00 9 0.016635
315.00 9 0.016635
33.00 9 0.016635
456.00 9 0.016635
59.00 9 0.016635
47.00 8 0.014787
365.00 8 0.014787
2400.00 8 0.014787
305.00 8 0.014787
129.00 8 0.014787
285.00 8 0.014787
119.00 8 0.014787
281.00 8 0.014787
217.00 8 0.014787
301.00 8 0.014787
166.00 8 0.014787
79.00 8 0.014787
118.00 8 0.014787
256.00 8 0.014787
211.00 8 0.014787
23.00 8 0.014787
142.00 8 0.014787
131.00 8 0.014787
415.00 8 0.014787
68.00 8 0.014787
17.00 7 0.012938
501.00 7 0.012938
356.00 7 0.012938
202.00 7 0.012938
77.00 7 0.012938
13.00 7 0.012938
575.00 7 0.012938
67.00 7 0.012938
84.00 7 0.012938
203.00 7 0.012938
239.00 7 0.012938
255.00 7 0.012938
132.00 7 0.012938
71.00 7 0.012938
73.00 7 0.012938
1700.00 7 0.012938
725.00 7 0.012938
470.00 7 0.012938
216.00 6 0.011090
186.00 6 0.011090
104.00 6 0.011090
199.00 6 0.011090
38.00 6 0.011090
124.00 6 0.011090
775.00 6 0.011090
58.00 6 0.011090
2100.00 6 0.011090
161.00 6 0.011090
106.00 5 0.009242
610.00 5 0.009242
19.00 5 0.009242
169.00 5 0.009242
2700.00 5 0.009242
82.00 5 0.009242
620.00 5 0.009242
510.00 5 0.009242
2800.00 5 0.009242
460.00 5 0.009242
660.00 5 0.009242
168.00 5 0.009242
188.00 5 0.009242
74.00 5 0.009242
1350.00 5 0.009242
606.00 5 0.009242
326.00 5 0.009242
141.00 5 0.009242
94.00 5 0.009242
136.00 5 0.009242
395.00 5 0.009242
54.00 5 0.009242
108.00 5 0.009242
3500.00 5 0.009242
98.00 5 0.009242
374.00 5 0.009242
825.00 5 0.009242
20000.00 5 0.009242
181.00 5 0.009242
925.00 5 0.009242
264.00 5 0.009242
406.00 5 0.009242
171.00 5 0.009242
149.00 4 0.007393
258.00 4 0.007393
465.00 4 0.007393
1150.00 4 0.007393
353.00 4 0.007393
540.00 4 0.007393
381.00 4 0.007393
299.00 4 0.007393
123.00 4 0.007393
265.00 4 0.007393
780.00 4 0.007393
1140.00 4 0.007393
351.00 4 0.007393
2300.00 4 0.007393
580.00 4 0.007393
206.00 4 0.007393
345.00 4 0.007393
146.00 4 0.007393
690.00 4 0.007393
530.00 4 0.007393
630.00 4 0.007393
316.00 4 0.007393
176.00 4 0.007393
7000.00 4 0.007393
401.00 4 0.007393
246.00 4 0.007393
820.00 4 0.007393
7500.00 4 0.007393
177.00 4 0.007393
87.00 4 0.007393
278.00 4 0.007393
37.00 4 0.007393
189.00 4 0.007393
3200.00 4 0.007393
251.00 4 0.007393
1750.00 4 0.007393
109.00 4 0.007393
147.00 4 0.007393
302.00 4 0.007393
221.00 3 0.005545
235.00 3 0.005545
261.00 3 0.005545
505.00 3 0.005545
152.00 3 0.005545
134.00 3 0.005545
376.00 3 0.005545
262.00 3 0.005545
373.00 3 0.005545
122.00 3 0.005545
570.00 3 0.005545
9000.00 3 0.005545
399.00 3 0.005545
539.00 3 0.005545
271.00 3 0.005545
573.00 3 0.005545
468.00 3 0.005545
226.00 3 0.005545
197.00 3 0.005545
249.00 3 0.005545
2200.00 3 0.005545
43.00 3 0.005545
1650.00 3 0.005545
44.00 3 0.005545
710.00 3 0.005545
204.00 3 0.005545
252.00 3 0.005545
112.00 3 0.005545
11000.00 3 0.005545
524.00 3 0.005545
1550.00 3 0.005545
489.00 3 0.005545
231.00 3 0.005545
556.00 3 0.005545
361.00 3 0.005545
162.00 3 0.005545
445.00 3 0.005545
107.00 3 0.005545
473.00 3 0.005545
143.00 3 0.005545
790.00 3 0.005545
144.00 3 0.005545
615.00 3 0.005545
334.00 3 0.005545
297.00 3 0.005545
506.00 3 0.005545
8000.00 3 0.005545
114.00 3 0.005545
975.00 3 0.005545
307.00 3 0.005545
3400.00 3 0.005545
1325.00 3 0.005545
2600.00 3 0.005545
223.00 3 0.005545
229.00 2 0.003697
338.00 2 0.003697
254.00 2 0.003697
93.00 2 0.003697
720.00 2 0.003697
354.00 2 0.003697
496.00 2 0.003697
920.00 2 0.003697
405.00 2 0.003697
1003.00 2 0.003697
687.00 2 0.003697
222.00 2 0.003697
1025.00 2 0.003697
756.00 2 0.003697
344.00 2 0.003697
194.00 2 0.003697
1580.00 2 0.003697
266.00 2 0.003697
179.00 2 0.003697
49.00 2 0.003697
5250.00 2 0.003697
103.00 2 0.003697
2900.00 2 0.003697
133.00 2 0.003697
187.00 2 0.003697
3100.00 2 0.003697
269.00 2 0.003697
238.00 2 0.003697
875.00 2 0.003697
219.00 2 0.003697
3650.00 2 0.003697
1175.00 2 0.003697
1075.00 2 0.003697
83.00 2 0.003697
324.00 2 0.003697
276.00 2 0.003697
153.00 2 0.003697
173.00 2 0.003697
429.00 2 0.003697
99.99 2 0.003697
5400.00 2 0.003697
25000.00 2 0.003697
564.00 2 0.003697
303.00 2 0.003697
57.00 2 0.003697
12000.00 2 0.003697
348.00 2 0.003697
1460.00 2 0.003697
159.00 2 0.003697
241.00 2 0.003697
207.00 2 0.003697
355.00 2 0.003697
565.00 2 0.003697
196.00 2 0.003697
6500.00 2 0.003697
331.00 2 0.003697
640.00 2 0.003697
495.00 2 0.003697
384.00 2 0.003697
1125.00 2 0.003697
455.00 2 0.003697
595.00 2 0.003697
16000.00 2 0.003697
483.00 2 0.003697
840.00 2 0.003697
816.00 2 0.003697
1020.00 2 0.003697
1950.00 2 0.003697
670.00 2 0.003697
444.00 2 0.003697
1968.00 2 0.003697
234.00 2 0.003697
1260.00 2 0.003697
689.00 2 0.003697
263.00 2 0.003697
172.00 2 0.003697
448.00 2 0.003697
514.00 2 0.003697
4500.00 2 0.003697
97.00 2 0.003697
590.00 2 0.003697
323.00 2 0.003697
2750.00 2 0.003697
228.00 2 0.003697
30.05 2 0.003697
244.00 2 0.003697
770.00 2 0.003697
402.00 2 0.003697
3600.00 2 0.003697
208.00 2 0.003697
337.00 2 0.003697
333.00 2 0.003697
218.00 2 0.003697
378.00 2 0.003697
631.00 2 0.003697
1900.00 2 0.003697
645.00 1 0.001848
3300.00 1 0.001848
651.00 1 0.001848
404.00 1 0.001848
1010.00 1 0.001848
328.00 1 0.001848
127.00 1 0.001848
873.00 1 0.001848
643.00 1 0.001848
5636.00 1 0.001848
382.00 1 0.001848
157.00 1 0.001848
343.00 1 0.001848
686.00 1 0.001848
705.00 1 0.001848
540.90 1 0.001848
379.00 1 0.001848
5003.00 1 0.001848
209.00 1 0.001848
385.00 1 0.001848
1559.18 1 0.001848
1270.00 1 0.001848
416.00 1 0.001848
9500.00 1 0.001848
28277.52 1 0.001848
227.00 1 0.001848
945.70 1 0.001848
566.00 1 0.001848
2190.00 1 0.001848
417.00 1 0.001848
2020.00 1 0.001848
1015.00 1 0.001848
494.00 1 0.001848
4100.00 1 0.001848
248.00 1 0.001848
936.00 1 0.001848
457.00 1 0.001848
293.00 1 0.001848
3420.00 1 0.001848
752.00 1 0.001848
584.00 1 0.001848
783.00 1 0.001848
1286.80 1 0.001848
74000.00 1 0.001848
308.00 1 0.001848
1056.00 1 0.001848
312.00 1 0.001848
639.00 1 0.001848
133.03 1 0.001848
461.00 1 0.001848
492.00 1 0.001848
6550.00 1 0.001848
13500.00 1 0.001848
692.85 1 0.001848
272.00 1 0.001848
1101.00 1 0.001848
228.70 1 0.001848
841.00 1 0.001848
795.00 1 0.001848
523.00 1 0.001848
346.00 1 0.001848
398.00 1 0.001848
7.20 1 0.001848
814.00 1 0.001848
1111.00 1 0.001848
247.00 1 0.001848
880.00 1 0.001848
2001.00 1 0.001848
11.11 1 0.001848
4301.00 1 0.001848
469.00 1 0.001848
162.67 1 0.001848
504.00 1 0.001848
393.00 1 0.001848
895.00 1 0.001848
439.00 1 0.001848
2575.55 1 0.001848
267.00 1 0.001848
164.00 1 0.001848
1773.00 1 0.001848
397.00 1 0.001848
507.00 1 0.001848
458.00 1 0.001848
730.00 1 0.001848
1554.00 1 0.001848
224.00 1 0.001848
739.00 1 0.001848
502.00 1 0.001848
709.00 1 0.001848
0.03 1 0.001848
467.00 1 0.001848
1443.00 1 0.001848
309.00 1 0.001848
1128.00 1 0.001848
1301.00 1 0.001848
243.00 1 0.001848
2350.00 1 0.001848
662.00 1 0.001848
890.00 1 0.001848
718.00 1 0.001848
1850.00 1 0.001848
1675.00 1 0.001848
588.00 1 0.001848
985.00 1 0.001848
233.00 1 0.001848
1830.00 1 0.001848
1336.00 1 0.001848
16.60 1 0.001848
182.00 1 0.001848
1.50 1 0.001848
7600.00 1 0.001848
418.32 1 0.001848
657.00 1 0.001848
4.61 1 0.001848
3262.00 1 0.001848
287.00 1 0.001848
1215.00 1 0.001848
1080.00 1 0.001848
3936.00 1 0.001848
648.00 1 0.001848
377.00 1 0.001848
180.50 1 0.001848
274.00 1 0.001848
184.00 1 0.001848
929.00 1 0.001848
193.00 1 0.001848
383.00 1 0.001848
1570.00 1 0.001848
1340.00 1 0.001848
3450.00 1 0.001848
18000.00 1 0.001848
369.00 1 0.001848
893.00 1 0.001848
591.00 1 0.001848
213.00 1 0.001848
7.38 1 0.001848
327.00 1 0.001848
3.13 1 0.001848
579.00 1 0.001848
434.00 1 0.001848
1418.00 1 0.001848
699.00 1 0.001848
17000.00 1 0.001848
431.00 1 0.001848
680.00 1 0.001848
0.57 1 0.001848
1120.00 1 0.001848
726.00 1 0.001848
486.00 1 0.001848
1620.00 1 0.001848
51.96 1 0.001848
516.00 1 0.001848
2250.00 1 0.001848
576.20 1 0.001848
488.00 1 0.001848
341.00 1 0.001848
368.00 1 0.001848
5200.00 1 0.001848
359.00 1 0.001848
331.10 1 0.001848
268.00 1 0.001848
15000.00 1 0.001848
352.00 1 0.001848
41.50 1 0.001848
113.00 1 0.001848
623.00 1 0.001848
1526.00 1 0.001848
568.00 1 0.001848
44.44 1 0.001848
40000.00 1 0.001848
810.00 1 0.001848
968.00 1 0.001848
29.00 1 0.001848
1171.00 1 0.001848
6845.00 1 0.001848
2802.00 1 0.001848
1515.60 1 0.001848
1473.00 1 0.001848
1273.00 1 0.001848
655.00 1 0.001848
4180.00 1 0.001848
298.00 1 0.001848
324.98 1 0.001848
4300.00 1 0.001848
2185.00 1 0.001848
881.00 1 0.001848
1217.00 1 0.001848
1001.00 1 0.001848
304.00 1 0.001848
695.00 1 0.001848
581.00 1 0.001848
421.00 1 0.001848
1625.00 1 0.001848
1.59 1 0.001848
0.02 1 0.001848
414.00 1 0.001848
889.00 1 0.001848
236.00 1 0.001848
259.00 1 0.001848
2450.00 1 0.001848
150.25 1 0.001848
558.00 1 0.001848
3700.00 1 0.001848
860.00 1 0.001848
1875.00 1 0.001848
7300.00 1 0.001848
2050.00 1 0.001848
508.00 1 0.001848
3901.00 1 0.001848
1375.00 1 0.001848
1315.00 1 0.001848
1525.00 1 0.001848
548.00 1 0.001848
371.00 1 0.001848
191.00 1 0.001848
5600.00 1 0.001848
451.00 1 0.001848
449.00 1 0.001848
167.00 1 0.001848
3050.00 1 0.001848
100000.00 1 0.001848
2570.00 1 0.001848
10.10 1 0.001848
48.08 1 0.001848
339.00 1 0.001848
5700.00 1 0.001848
2575.00 1 0.001848
1578.00 1 0.001848
317.00 1 0.001848
294.00 1 0.001848
745.00 1 0.001848
485.00 1 0.001848
459.00 1 0.001848
335.00 1 0.001848
363.00 1 0.001848
1209.00 1 0.001848
1070.00 1 0.001848
2177.00 1 0.001848
389.00 1 0.001848
1725.00 1 0.001848
1379.00 1 0.001848
970.00 1 0.001848
674.00 1 0.001848
665.00 1 0.001848
518.00 1 0.001848
137.00 1 0.001848
823.00 1 0.001848
479.00 1 0.001848
1153.00 1 0.001848
958.00 1 0.001848
3.30 1 0.001848
806.00 1 0.001848
296.00 1 0.001848
msf_lastyeardonorvalue__c: suma de las aportaciones de los ultimos 365 dias.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_maximumdonorvalue__c: Variable numerica
In [790]:
# Vamos a realizar analisis por cada variable
var = "msf_maximumdonorvalue__c"
In [791]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_maximumdonorvalue__c es 727510. Lo que supone un 73.18542870479165%
El nº de vacios para la variable msf_maximumdonorvalue__c es 0. Lo que supone un 0.0%
Out[791]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_recencydonorcont__c',
 'msf_valuedonorcont__c',
 'msf_lastyeardonorvalue__c',
 'msf_maximumdonorvalue__c']
In [792]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[792]:
# Tot % Tot
60.00 32588 12.225665
100.00 30759 11.539500
30.00 28663 10.753168
50.00 22916 8.597132
20.00 19944 7.482161
... ... ...
26.14 1 0.000375
17.63 1 0.000375
19.48 1 0.000375
2800.00 1 0.000375
1.17 1 0.000375

2101 rows × 2 columns

msf_maximumdonorvalue__c: importe más elevado de todos los donativos.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_averagedonorvalue__c: Variable numerica
In [793]:
# Vamos a realizar analisis por cada variable
var = "msf_averagedonorvalue__c"
In [794]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_averagedonorvalue__c es 727510. Lo que supone un 73.18542870479165%
El nº de vacios para la variable msf_averagedonorvalue__c es 0. Lo que supone un 0.0%
Out[794]:
['npo02__best_gift_year__c',
 'msf_birthyear__c',
 'msf_firstcampaigncolaborationchannel__c',
 'npo02__averageamount__c',
 'msf_isactivedonor__c',
 'msf_isactiverecurringdonor__c',
 'msf_datefirstdonation__c',
 'msf_datelastdonation__c',
 'npsp__largest_soft_credit_date__c',
 'npsp__first_soft_credit_date__c',
 'npsp__last_soft_credit_date__c',
 'msf_lastrecurringdonationdate__c',
 'npo02__lastclosedate__c',
 'npsp__first_soft_credit_amount__c',
 'npsp__last_soft_credit_amount__c',
 'msf_annualizedquotachange__c',
 'npsp__largest_soft_credit_amount__c',
 'npo02__soft_credit_last_year__c',
 'npo02__soft_credit_this_year__c',
 'npo02__soft_credit_two_years_ago__c',
 'msf_recencydonorcont__c',
 'msf_valuedonorcont__c',
 'msf_lastyeardonorvalue__c',
 'msf_maximumdonorvalue__c',
 'msf_averagedonorvalue__c']
In [795]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[795]:
# Tot % Tot
30.00 21243 7.969492
60.00 17261 6.475611
20.00 15879 5.957142
50.00 13184 4.946090
10.00 13002 4.877811
... ... ...
1392.86 1 0.000375
53.73 1 0.000375
644.44 1 0.000375
467.74 1 0.000375
128.50 1 0.000375

17627 rows × 2 columns

msf_averagedonorvalue__c: importe medio de todos los donativos.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_lifetime__c: Variable numerica
In [796]:
# Vamos a realizar analisis por cada variable
var = "msf_lifetime__c"
In [797]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lifetime__c es 57038. Lo que supone un 5.73785993658356%
El nº de vacios para la variable msf_lifetime__c es 0. Lo que supone un 0.0%
In [798]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[798]:
# Tot % Tot
0.0 154514 16.489831
1.0 75815 8.091024
2.0 59321 6.330774
6.0 56779 6.059490
3.0 55521 5.925236
7.0 55381 5.910295
5.0 52727 5.627058
8.0 52462 5.598777
4.0 52176 5.568255
9.0 36174 3.860512
10.0 29908 3.191800
11.0 28918 3.086147
12.0 26702 2.849654
13.0 24708 2.636853
14.0 22563 2.407937
17.0 17781 1.897599
18.0 17439 1.861101
16.0 16506 1.761531
15.0 15327 1.635707
19.0 13700 1.462073
20.0 10915 1.164856
28.0 10427 1.112776
23.0 8673 0.925588
22.0 6923 0.738827
24.0 6358 0.678530
21.0 5587 0.596248
29.0 5515 0.588564
25.0 4929 0.526026
26.0 4213 0.449614
27.0 4134 0.441183
30.0 3745 0.399669
31.0 707 0.075451
32.0 182 0.019423
34.0 138 0.014727
33.0 97 0.010352
35.0 47 0.005016
36.0 14 0.001494
msf_lifetime__c: numero de años enteros desde primera aportacion a la ultima.
Se puede observar que .... vacios.
Analsis de distribución por variables
-> msf_commitment__c: Variable numerica
In [799]:
# Vamos a realizar analisis por cada variable
var = "msf_commitment__c"
In [800]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_commitment__c es 20360. Lo que supone un 2.0481578650871572%
El nº de vacios para la variable msf_commitment__c es 0. Lo que supone un 0.0%
In [801]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[801]:
# Tot % Tot
0.0 645886 66.332890
1.0 185025 19.002181
2.0 72014 7.395882
3.0 32348 3.322160
4.0 16454 1.689836
5.0 8691 0.892571
6.0 5006 0.514119
7.0 2974 0.305432
8.0 1781 0.182910
9.0 1118 0.114819
10.0 679 0.069734
11.0 489 0.050221
12.0 305 0.031324
13.0 219 0.022491
14.0 155 0.015919
16.0 105 0.010784
15.0 104 0.010681
17.0 68 0.006984
18.0 46 0.004724
19.0 35 0.003595
20.0 27 0.002773
21.0 26 0.002670
22.0 20 0.002054
23.0 16 0.001643
24.0 14 0.001438
25.0 13 0.001335
29.0 12 0.001232
26.0 10 0.001027
30.0 8 0.000822
28.0 8 0.000822
27.0 7 0.000719
32.0 7 0.000719
31.0 6 0.000616
33.0 4 0.000411
34.0 3 0.000308
43.0 2 0.000205
38.0 2 0.000205
36.0 2 0.000205
42.0 2 0.000205
61.0 2 0.000205
47.0 1 0.000103
72.0 1 0.000103
57.0 1 0.000103
80.0 1 0.000103
56.0 1 0.000103
37.0 1 0.000103
71.0 1 0.000103
35.0 1 0.000103
46.0 1 0.000103
45.0 1 0.000103
54.0 1 0.000103
msf_commitment__c: suma de iteraciones.
Se puede observar que .... vacios.
In [ ]:
 

4. Tabla Campaña¶

In [802]:
# Vamos a analizar la tabla Campañas
df = df_Campaign
In [803]:
# Se crea una lista por ahora vacia, en la que se irán añadiendo las variables que se van a eliminar del dataset por motivos varios: no utilidad, gran volumen de nulos, ...
col_to_delete_campaign=list()
Analsis de distribución por variables
-> id: Variable String
In [804]:
# Vamos a realizar analisis por cada variable
var = "id"
In [805]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable id es 0. Lo que supone un 0.0%
El nº de vacios para la variable id es 0. Lo que supone un 0.0%
In [806]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[806]:
# Tot % Tot
7013Y000001mrHWQAY 1 0.008695
7013Y000001nEG3QAM 1 0.008695
7013Y000001vaDMQAY 1 0.008695
7013Y000001va6KQAQ 1 0.008695
7013Y000001vaDbQAI 1 0.008695
... ... ...
7013Y0000011TdzQAE 1 0.008695
7013Y000001mrhvQAA 1 0.008695
7013Y000001mri3QAA 1 0.008695
7013Y000001mriBQAQ 1 0.008695
7013Y0000011VNkQAM 1 0.008695

11501 rows × 2 columns

msf_currentcampaign__c: Campaña actual de la aportación.
Exite un 0% de vacios. Existe un identificador unico por cada registro de la tabla.
In [807]:
# Vamos a realizar analisis por cada variable
var = "id"
In [808]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable id es 0. Lo que supone un 0.0%
El nº de vacios para la variable id es 0. Lo que supone un 0.0%
In [809]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[809]:
# Tot % Tot
7013Y000001mrHWQAY 1 0.008695
7013Y000001nEG3QAM 1 0.008695
7013Y000001vaDMQAY 1 0.008695
7013Y000001va6KQAQ 1 0.008695
7013Y000001vaDbQAI 1 0.008695
... ... ...
7013Y0000011TdzQAE 1 0.008695
7013Y000001mrhvQAA 1 0.008695
7013Y000001mri3QAA 1 0.008695
7013Y000001mriBQAQ 1 0.008695
7013Y0000011VNkQAM 1 0.008695

11501 rows × 2 columns

msf_currentcampaign__c: Campaña actual de la aportación.
Exite un 0% de vacios. Existe un identificador unico por cada registro de la tabla.
Analsis de distribución por variables
-> msf_attribute_1__c: Variable Char
In [810]:
# Vamos a realizar analisis por cada variable
var = "msf_attribute_1__c"
In [811]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_attribute_1__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_attribute_1__c es 4816. Lo que supone un 41.8746195982958%
Out[811]:
['msf_attribute_1__c']
In [812]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[812]:
# Tot % Tot
4816 41.874620
birthday 1308 11.372924
Camp trimestral 549 4.773498
Cumpleaños 539 4.686549
sports_event 533 4.634380
other 454 3.947483
cultural_event 339 2.947570
Otros 252 2.191114
F2F interno 149 1.295540
F2F externo 125 1.086862
Evento deportivo 102 0.886879
Google Search 100 0.869490
Mercadillo 94 0.817320
Newsletter 94 0.817320
Representación artística 90 0.782541
Encuentros solidarios 77 0.669507
Facebook 69 0.599948
El Pais 48 0.417355
Reto personal 47 0.408660
Regalo solidario 42 0.365186
Bajas 35 0.304321
Revista 34 0.295626
Afiliación Leads 34 0.295626
Tele5 33 0.286932
TV (no sabe) 30 0.260847
corporate 29 0.252152
TVE-1 29 0.252152
National Geographic 28 0.243457
Banner Web 27 0.234762
Evento solidario 27 0.234762
Antena3 26 0.226067
Aniversario 25 0.217372
Venca 25 0.217372
anniversary 25 0.217372
La Vanguardia 24 0.208678
Giving tuesday 23 0.199983
in_memory_of 23 0.199983
El Correo 23 0.199983
Telefono encartes 22 0.191288
TVE-2 21 0.182593
La voz de galicia 19 0.165203
Iniciativa de Empresa 18 0.156508
Cuatro 18 0.156508
Evento 18 0.156508
National Geographic historia 17 0.147813
El Periodico 17 0.147813
Mercadillo Solidario 17 0.147813
No sabe 17 0.147813
902 deles 16 0.139118
El Mundo 16 0.139118
ABC 16 0.139118
Muy Interesante 16 0.139118
Diario de navarra 15 0.130423
TV-3 15 0.130423
Evento Cultural 15 0.130423
National Geographic viajes 15 0.130423
TV delegaciones 14 0.121729
Heraldo de Aragón 14 0.121729
Concierto solidario 13 0.113034
Expositor bancos 13 0.113034
Diario vasco 13 0.113034
Faro de vigo-pontevedra 12 0.104339
BBVA 12 0.104339
En memoria de 12 0.104339
Diario de mallorca 12 0.104339
Sur 12 0.104339
Encarte bancos 11 0.095644
Levante 11 0.095644
La nueva españa-oviedo 10 0.086949
Publicidad gratuita 10 0.086949
Regala salud 10 0.086949
Impagos 10 0.086949
La provincia-canarias 10 0.086949
Google Display 10 0.086949
Diario de noticias (navarra) 9 0.078254
SMS 3a llamada 9 0.078254
D2D interno 9 0.078254
Las provincias 9 0.078254
La verdad 9 0.078254
Noticias de gipuzkoa 8 0.069559
Geo 8 0.069559
Diario de tarragona 8 0.069559
Diario montañes 8 0.069559
Diario de leon 8 0.069559
Diario de menorca 8 0.069559
Norte de castilla 8 0.069559
Deia noticias de bizkaia 8 0.069559
El comercio 8 0.069559
Hoy 8 0.069559
Diario de burgos 7 0.060864
Facebook messenger 7 0.060864
De viajes 7 0.060864
La opinion de tenerife 7 0.060864
Empresas 7 0.060864
Telemadrid 7 0.060864
Diario de ibiza 7 0.060864
Via digital 7 0.060864
La rioja 7 0.060864
Ideal 7 0.060864
Facebook Lead 7 0.060864
Saber vivir 7 0.060864
Exposición solidaria 6 0.052169
Bromera 6 0.052169
SMS 4a llamada 6 0.052169
Racc 6 0.052169
Pastillas contra dolor ajeno 6 0.052169
Invisibles en el pais 6 0.052169
902 internet 6 0.052169
Clara 6 0.052169
Revista cottet 6 0.052169
Canal satelite digital 6 0.052169
Diario de noticias de alava 6 0.052169
Integral 6 0.052169
Audi magazine 6 0.052169
Informacion 6 0.052169
Encarte atrapados 2002 5 0.043474
La opinion de zamora 5 0.043474
Pulsa 5 0.043474
Muy interesante historia 5 0.043474
Desnutrición 5 0.043474
Que leer 5 0.043474
La opinion de murcia 5 0.043474
Mutua scias 5 0.043474
SMS 2a llamada 5 0.043474
Prospectos 5 0.043474
SMS 5a llamada 4 0.034780
Google Youtube 4 0.034780
Manga films 4 0.034780
Diario de avila 4 0.034780
D2D externo 4 0.034780
Mi bebe y yo 4 0.034780
El jueves 4 0.034780
Sfera 4 0.034780
Haiti 4 0.034780
Emprendedores 4 0.034780
Reto deportivo 4 0.034780
Descobrir Catalunya 4 0.034780
Programática 4 0.034780
Infantil 3 0.026085
Autonomos 3 0.026085
Radio generico 3 0.026085
ADSALSA NO LLAMADOS ONG 3 0.026085
Diez minutos 3 0.026085
La opinion de a coruña 3 0.026085
XLSemanaL 3 0.026085
Comunishop movil 3 0.026085
Historia National Geographic 3 0.026085
Mia 3 0.026085
Revista ajuntament 3 0.026085
Delicatessen 3 0.026085
Webpilots 3 0.026085
Slider Web 3 0.026085
Clio 3 0.026085
La mañana de lerida 3 0.026085
Ana rosa 3 0.026085
Whatsapp 3 0.026085
Canarias 7 3 0.026085
ABSERT 3 0.026085
Fotogramas 3 0.026085
DVD invisibles el pais 3 0.026085
AD735 3 0.026085
Citibank 3 0.026085
Mundo oferta 3 0.026085
Tc Teléfono Fidelización 2 0.017390
Content ignition 2 0.017390
Comer y beber 2 0.017390
Tiktok 2 0.017390
La nueva españa 2 0.017390
Rutas del mundo 2 0.017390
Coregistro 2 0.017390
CENTRO NUTRICIONAL 2 0.017390
Viajes National Geographic 2 0.017390
Mediaset (T5+Cuatro) 2 0.017390
Abc (Nacional) 2 0.017390
El Periodico (Dominical) 2 0.017390
Mente sana 2 0.017390
Pc actual 2 0.017390
Diari de levante 2 0.017390
Ser padres hoy 2 0.017390
Ex circulo de lectores 2 0.017390
Diario avisos 2 0.017390
La Mañana De Lérida 2 0.017390
Diario de cadiz 2 0.017390
Historia y vida 2 0.017390
La sexta 2 0.017390
MEDIACOM/DIGITALCONTENT 2 0.017390
Banc Sabadell 2 0.017390
Mediterraneo 2 0.017390
Diario de jerez 2 0.017390
Avui 2 0.017390
Hoy badajoz 2 0.017390
Prensa iberica 2 0.017390
Planet 49 2 0.017390
Ra cadena ser 2 0.017390
Lonely planet 2 0.017390
Espontáneos 902 250 902 2 0.017390
Descobrir cuina 2 0.017390
Yemen 2 0.017390
Yate 2 0.017390
Revista lecturas 2 0.017390
Col medicos alicante 2 0.017390
Magazine - Mercedes Benz 2 0.017390
Recomendación conocidos 2 0.017390
Quo 2 0.017390
Sapiens 2 0.017390
Diario De León 2 0.017390
Tienda online 2 0.017390
Viajar 2 0.017390
Instagram 2 0.017390
Regalo de gran valor 2 0.017390
Twitter 1 0.008695
Mas alla 1 0.008695
Tc Teléfono Mailings 1 0.008695
El País (Eps) 1 0.008695
Cinco dias 1 0.008695
Bienvenida 1 0.008695
Colegios profesionales cantabria ju 1 0.008695
Tm Telefono web 1 0.008695
Diario Montañés 1 0.008695
Speak up 1 0.008695
Arquitectura y diseño 1 0.008695
Compractica movil 1 0.008695
Ultima hora 1 0.008695
Información 1 0.008695
Cupón Solicitado Por El Interesado 1 0.008695
Fundaciones 1 0.008695
El mueble 1 0.008695
Altair 1 0.008695
Muy Interesante-Historia 1 0.008695
Tc Teléfono Atención Al Socio 1 0.008695
Historia de iberia vieja 1 0.008695
Marie claire 1 0.008695
eldiario.es 1 0.008695
Mercedes benz 1 0.008695
Runners World 1 0.008695
Col med valencia 1 0.008695
Teléfono atención al Socio 1 0.008695
HERALDO DE ARAGON 1 0.008695
Nativa 1 0.008695
Car & driver 1 0.008695
El País (Eps) Cataluña 1 0.008695
Bing 1 0.008695
El Pais (Eps) Cataluña 1 0.008695
Internet cupon 1 0.008695
13TV 1 0.008695
Col ats alava 1 0.008695
La Opinión De Tenerife 1 0.008695
Exsocios 1 0.008695
Barclays 1 0.008695
Herencias 1 0.008695
Muy interesante+geo 1 0.008695
Cupon mundo oferta 1 0.008695
Canal 33 1 0.008695
Aleatorio score 0-0,5 tel 1 0.008695
Tc Teléfono Encartes 1 0.008695
Mailing 1 0.008695
El Dia Tenerife 1 0.008695
La opinion de malaga 1 0.008695
C.O. PLAZA MAYOR 1 0.008695
Soc no rec sin extra valor bajo 1 0.008695
El Mundo Catalunya 1 0.008695
Desconocido 902 250 902 1 0.008695
Col med la rioja 1 0.008695
Tv Castilla La Mancha 1 0.008695
S no rec sin ext val b 1 0.008695
La Opinión De Murcia 1 0.008695
Tc Telefono Fidelizacion 1 0.008695
Caja abogados 1 0.008695
Donantes 1er año valor alto 1 0.008695
Cuerpo mente 1 0.008695
Crecer feliz 1 0.008695
CHARLA 1 0.008695
EITB 1 0.008695
Labores 1 0.008695
Clasicos exclusivos 1 0.008695
Profesionales 1 0.008695
Wc Clara 1 0.008695
Tc Telefono Mailings 1 0.008695
Cupon Solicitado Por El Interesado 1 0.008695
Tele 5 1 0.008695
Interiores 1 0.008695
La redoute 1 0.008695
El Pais (Eps) 1 0.008695
Ara 1 0.008695
Publicación solidaria 1 0.008695
E-mailing angola junio 02 1 0.008695
Beef! 1 0.008695
El Diario de Sevilla 1 0.008695
Cine Documental 1 0.008695
Llamada devos econ entropia 1 0.008695
Pastillas - doc farmacias 1 0.008695
Entidad financiera 1 0.008695
QUIENES SOMOS 1 0.008695
La Opinión De Málaga 1 0.008695
Diario De Ávila 1 0.008695
Europa sur 1 0.008695
socios con cambio cuota rec valor a 1 0.008695
Wc Beef! 1 0.008695
Internet 1 0.008695
El Dia De Soria 1 0.008695
Hea valencia 1 0.008695
CAPTACION EN LA CALLE 1 0.008695
Objetivo bienestar 1 0.008695
msf_attribute_1__c: Campaña actual de la aportación.
Exite un 42% de vacios. Hay muchisima distribución entre los diferentes id de campaña.
Analsis de distribución por variables
-> msf_attribute_2__c: Variable Char
In [813]:
# Vamos a realizar analisis por cada variable
var = "msf_attribute_2__c"
In [814]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_attribute_2__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_attribute_2__c es 7264. Lo que supone un 63.15972524128337%
Out[814]:
['msf_attribute_1__c', 'msf_attribute_2__c']
In [815]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[815]:
# Tot % Tot
7264 63.159725
Genérico 1083 9.416572
Desnut 796 6.921137
Coronavirus 734 6.382054
FE 339 2.947570
Refugiados 260 2.260673
Camp trimestral octubre 200 1.738979
Camp trimestral fiscal 121 1.052082
Camp trimestral navidad 116 1.008608
Vacunación 109 0.947744
Camp trimestral junio 87 0.756456
Madrid 34 0.295626
Email (Lead) 32 0.278237
No Sabe 31 0.269542
Facebook (Lead) 28 0.243457
Camp trimestral 25 0.217372
Barcelona 20 0.173898
Bilbao 15 0.130423
Valencia 12 0.104339
Corregistro (Lead) 11 0.095644
Murcia 10 0.086949
Sevilla 8 0.069559
Málaga 8 0.069559
A Coruña 7 0.060864
Pamplona 6 0.052169
Mallorca 6 0.052169
Tenerife 6 0.052169
Santiago 5 0.043474
Retargeting 5 0.043474
Zaragoza 5 0.043474
Elche 4 0.034780
Lleida 4 0.034780
Jeréz 4 0.034780
Cádiz 4 0.034780
Siria 4 0.034780
San Sebastián 4 0.034780
Alicante 4 0.034780
Asturias 4 0.034780
Gran Canaria 4 0.034780
Vigo 4 0.034780
Extremadura 3 0.026085
Donosti 3 0.026085
Santander 3 0.026085
Salamanca 3 0.026085
Granada 3 0.026085
Toledo 3 0.026085
Segovia 3 0.026085
Córdoba 3 0.026085
La Palma 3 0.026085
Zamora 3 0.026085
Benicassim 2 0.017390
Ibiza 2 0.017390
Girona 2 0.017390
Tenerife Sur 2 0.017390
Puerto de Sta María 2 0.017390
Menorca 2 0.017390
Burgos 2 0.017390
Encuesta (Lead) 2 0.017390
Tossa De Mar 2 0.017390
Ciudad Real 2 0.017390
Valladolid 2 0.017390
Huesca 1 0.008695
Palencia 1 0.008695
Almería 1 0.008695
Lanzarote 1 0.008695
Redes Sociales (Lead) 1 0.008695
Pymes Madrid 1 0.008695
Fuerteventura 1 0.008695
Huelva 1 0.008695
Giving Tuesday 1 0.008695
Ceuta 1 0.008695
Logroño 1 0.008695
Tarragona 1 0.008695
Cuenca 1 0.008695
Display (Lead) 1 0.008695
Vitoria 1 0.008695
Melilla 1 0.008695
Castellon 1 0.008695
Norte 1 0.008695
Leon 1 0.008695
MALAGA 1 0.008695
San Sebastian 1 0.008695
Colera 1 0.008695
Memoria 1 0.008695
Guadalajara 1 0.008695
Castellón 1 0.008695
Camp trimestral diciembre 1 0.008695
msf_attribute_2__c: Campaña actual de la aportación.
Exite un 61% de vacios. Hay muchisima distribución entre los diferentes id de campaña.
Analsis de distribución por variables
-> msf_attribute_3__c: Variable Char
In [816]:
# Vamos a realizar analisis por cada variable
var = "msf_attribute_3__c"
In [817]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_attribute_3__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_attribute_3__c es 10652. Lo que supone un 92.61803321450309%
Out[817]:
['msf_attribute_1__c', 'msf_attribute_2__c', 'msf_attribute_3__c']
In [818]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[818]:
# Tot % Tot
10652 92.618033
Creadores Sin Fronteras 258 2.243283
Non-Branded 146 1.269455
Inbound 142 1.234675
Zona Centro 45 0.391270
No sabe 30 0.260847
Zona Catalunya 30 0.260847
Branded 29 0.252152
Zona País Vasco 24 0.208678
Zona Andalucía Occidental 21 0.182593
Zona Levante Sur 17 0.147813
Zona Canarias 17 0.147813
Zona Galicia 16 0.139118
Zona Levante Norte 15 0.130423
Zona Andalucía Oriental 13 0.113034
Zona Castilla Y Leon 12 0.104339
Zona Baleares 10 0.086949
Zona Navarra 6 0.052169
Zona Aragón 5 0.043474
Zona Extremadura 4 0.034780
Zona Asturias 3 0.026085
Zona Cantabria 3 0.026085
Referral 1 0.008695
Zona La Rioja 1 0.008695
Zona Pais Vasco 1 0.008695
In [ ]:
 
msf_attribute_3__c: Campaña actual de la aportación.
Exite un 93% de vacios. Hay muchisima distribución entre los diferentes id de campaña.
Analsis de distribución por variables
-> msf_attribute_4__c: Variable Char
In [819]:
# Vamos a realizar analisis por cada variable
var = "msf_attribute_4__c"
In [820]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_attribute_4__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_attribute_4__c es 10943. Lo que supone un 95.14824797843666%
Out[820]:
['msf_attribute_1__c',
 'msf_attribute_2__c',
 'msf_attribute_3__c',
 'msf_attribute_4__c']
msf_attribute_4__c: Campaña actual de la aportación.
Exite un 95% de vacios. Hay muchisima distribución entre los diferentes id de campaña.
Analsis de distribución por variables
-> msf_attribute_5__c: Variable Char
In [821]:
# Vamos a realizar analisis por cada variable
var = "msf_attribute_5__c"
In [822]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_attribute_5__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_attribute_5__c es 11223. Lo que supone un 97.58281888531431%
Out[822]:
['msf_attribute_1__c',
 'msf_attribute_2__c',
 'msf_attribute_3__c',
 'msf_attribute_4__c',
 'msf_attribute_5__c']
msf_attribute_5__c: Campaña actual de la aportación.
Exite un 98% de vacios. Hay muchisima distribución entre los diferentes id de campaña.
Analsis de distribución por variables
-> msf_campaigndonationreporting__c: Variable Char
In [823]:
# Vamos a realizar analisis por cada variable
var = "msf_campaigndonationreporting__c"
In [824]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_campaigndonationreporting__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_campaigndonationreporting__c es 42. Lo que supone un 0.36518563603164944%
In [825]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[825]:
# Tot % Tot
16-Captación off resto 3920 34.083993
13-Iniciativa Solidaria Online 3356 29.180071
12-Iniciativa Solidaria off line 867 7.538475
32-Mailing fide 494 4.295279
33-Emailing fide 438 3.808364
34-Officers Mid plus 379 3.295366
31-Telemarketing fide 303 2.634554
11-Tlmk captación 285 2.478045
15-Televisión 261 2.269368
23-Digital Orgánico 185 1.608556
53-Resto 176 1.530302
22-Digital Publi 173 1.504217
52-Desconocido off 169 1.469437
21-Digital leads (email/tlmk) 142 1.234675
41-Officers Grandes empresas 90 0.782541
35-Tlmk Mid 79 0.686897
42-Officers Grandes donantes 66 0.573863
36-Fide resto 51 0.443440
42 0.365186
14-Celebraciones 24 0.208678
43-Officers Grandes fundaciones 1 0.008695
msf_campaigndonationreporting__c
Exite un 0.36% de vacios. Hay una buena distribución en la muestra.
Analsis de distribución por variables
-> msf_campaignentryreporting__c: Variable Char
In [826]:
# Vamos a realizar analisis por cada variable
var = "msf_campaignentryreporting__c"
In [827]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_campaignentryreporting__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_campaignentryreporting__c es 42. Lo que supone un 0.36518563603164944%
In [828]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[828]:
# Tot % Tot
18-Captación off resto 8148 70.846013
31-Mailing revista memoria Fide 494 4.295279
33-Emailing fide 438 3.808364
34-Fide resto 430 3.738805
41-Resto 332 2.886706
32-Tlmk conversión 304 2.643248
23-Digital Orgánico 186 1.617251
22-Digital publi 173 1.504217
19-Desconocido off 169 1.469437
13-Tlmk frío 163 1.417268
11-F2F interno 162 1.408573
21-Digital leads (email/tlmk) 142 1.234675
12-F2F externo 125 1.086862
35-Tlmk reactivación bajas 68 0.591253
42 0.365186
14-Tlmk SMS DRTIV 41 0.356491
16-Tlmk rellamada 38 0.330406
17-Tlmk prospectos 23 0.199983
15-Tlmk sms otros 13 0.113034
36-Tlmk impagos 10 0.086949
msf_msf_campaignentryreporting__c
Exite un 0.36% de vacios. Hay una alta concentración en la muestra.
Analsis de distribución por variables
-> msf_canalsalidaconcatenado__c: Variable Char
In [829]:
# Vamos a realizar analisis por cada variable
var = "msf_canalsalidaconcatenado__c"
In [830]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_canalsalidaconcatenado__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_canalsalidaconcatenado__c es 0. Lo que supone un 0.0%
In [831]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[831]:
# Tot % Tot
Iniciativa Solidaria - 4227 36.753326
Publi Ext - 2270 19.737414
Encarte - 892 7.755847
TLMK - 763 6.634206
Mailing - 707 6.147292
Emailings - 518 4.503956
Officers - 472 4.103991
F2F - 283 2.460656
Televisión - 263 2.286758
Desconocido - 208 1.808538
Otros - 172 1.495522
- 119 1.034693
Prensa o cupón - 117 1.017303
Redes Sociales - 89 0.773846
Paid Search - 85 0.739066
Orgánico - 60 0.521694
Publicidad digital - 46 0.399965
Display - 40 0.347796
Afiliación - 35 0.304321
Celebraciones - 24 0.208678
Banners - 18 0.156508
Exposiciones - 17 0.147813
Email - 16 0.139118
Dipticos - 16 0.139118
D2D - 13 0.113034
SMS - 12 0.104339
Radio - 11 0.095644
Eventos - 4 0.034780
Mensajería Instantánea - 3 0.026085
Tienda MSF - 1 0.008695
msf_canalsalidaconcatenado__c
Exite un 0% de vacios. Existe una buena distribución en la muestra.
Analsis de distribución por variables
-> msf_campaignentryreporting__c: Variable Char
In [832]:
# Vamos a realizar analisis por cada variable
var = "msf_campaignentryreporting__c"
In [833]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_campaignentryreporting__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_campaignentryreporting__c es 42. Lo que supone un 0.36518563603164944%
In [834]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[834]:
# Tot % Tot
18-Captación off resto 8148 70.846013
31-Mailing revista memoria Fide 494 4.295279
33-Emailing fide 438 3.808364
34-Fide resto 430 3.738805
41-Resto 332 2.886706
32-Tlmk conversión 304 2.643248
23-Digital Orgánico 186 1.617251
22-Digital publi 173 1.504217
19-Desconocido off 169 1.469437
13-Tlmk frío 163 1.417268
11-F2F interno 162 1.408573
21-Digital leads (email/tlmk) 142 1.234675
12-F2F externo 125 1.086862
35-Tlmk reactivación bajas 68 0.591253
42 0.365186
14-Tlmk SMS DRTIV 41 0.356491
16-Tlmk rellamada 38 0.330406
17-Tlmk prospectos 23 0.199983
15-Tlmk sms otros 13 0.113034
36-Tlmk impagos 10 0.086949
msf_campaignentryreporting__c
Exite un 0% de vacios. Existe una alta concentracion.
Analsis de distribución por variables
-> msf_isemergency__c: Variable Char
In [835]:
# Vamos a realizar analisis por cada variable
var = "msf_isemergency__c"
In [836]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_isemergency__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_isemergency__c es 0. Lo que supone un 0.0%
In [837]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[837]:
# Tot % Tot
False 11138 96.843753
True 363 3.156247
msf_isemergency__c
Exite un 0% de vacios. Existe una alta concentracion. Por lo que se descarta la variable.
Analsis de distribución por variables
-> msf_isonline__c: Variable Char
In [838]:
# Vamos a realizar analisis por cada variable
var = "msf_isonline__c"
In [839]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_isonline__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_isonline__c es 139. Lo que supone un 1.2085905573428397%
In [840]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[840]:
# Tot % Tot
No 6891 59.916529
Si 4457 38.753152
139 1.208591
NA 14 0.121729
msf_isonline__c
Exite un 1.2% de vacios. Existe una buena distribucion.
Analsis de distribución por variables
-> msf_objective__c: Variable Char
In [841]:
# Vamos a realizar analisis por cada variable
var = "msf_objective__c"
In [842]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_objective__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_objective__c es 105. Lo que supone un 0.9129640900791236%
In [843]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[843]:
# Tot % Tot
Captación de socios o donantes 6618 57.542822
Captación de leads 2342 20.363447
Upgrade 1129 9.816538
Cultivación 494 4.295279
Desconocido 263 2.286758
Conversión 215 1.869403
Conversión (de lead o donante a socio) 215 1.869403
105 0.912964
Recuperación 85 0.739066
Otros 23 0.199983
Petición difusión 4 0.034780
Informativo 3 0.026085
Rendición de cuentas 3 0.026085
Fidelización 1 0.008695
Captación 1 0.008695
msf_isonline__c
Exite un 0.91% de vacios. Existe una buena distribucion.
Analsis de distribución por variables
-> msf_objectivepublic__c: Variable Char
In [844]:
# Vamos a realizar analisis por cada variable
var = "msf_objectivepublic__c"
In [845]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_objectivepublic__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_objectivepublic__c es 10272. Lo que supone un 89.31397269802626%
Out[845]:
['msf_attribute_1__c',
 'msf_attribute_2__c',
 'msf_attribute_3__c',
 'msf_attribute_4__c',
 'msf_attribute_5__c',
 'msf_objectivepublic__c']
In [846]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[846]:
# Tot % Tot
10272 89.313973
MASS 1229 10.686027
msf_objectivepublic__c
Exite un 89% de vacios. Por lo que se descarta la variable.
Analsis de distribución por variables
-> msf_outboundchannel1__c: Variable Char
In [847]:
# Vamos a realizar analisis por cada variable
var = "msf_outboundchannel1__c"
In [848]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_outboundchannel1__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_outboundchannel1__c es 119. Lo que supone un 1.0346926354230066%
In [849]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[849]:
# Tot % Tot
Iniciativa Solidaria 4227 36.753326
Publi Ext 2270 19.737414
Encarte 892 7.755847
TLMK 763 6.634206
Mailing 707 6.147292
Emailings 518 4.503956
Officers 472 4.103991
F2F 283 2.460656
Televisión 263 2.286758
Desconocido 208 1.808538
Otros 172 1.495522
119 1.034693
Prensa o cupón 117 1.017303
Redes Sociales 89 0.773846
Paid Search 85 0.739066
Orgánico 60 0.521694
Publicidad digital 46 0.399965
Display 40 0.347796
Afiliación 35 0.304321
Celebraciones 24 0.208678
Banners 18 0.156508
Exposiciones 17 0.147813
Email 16 0.139118
Dipticos 16 0.139118
D2D 13 0.113034
SMS 12 0.104339
Radio 11 0.095644
Eventos 4 0.034780
Mensajería Instantánea 3 0.026085
Tienda MSF 1 0.008695
msf_outboundchannel1__c
Exite un 1% de vacios. Existe una buena distribucion.
Analsis de distribución por variables
-> msf_outboundchannel2__c: Variable Char
In [850]:
# Vamos a realizar analisis por cada variable
var = "msf_outboundchannel2__c"
In [851]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_outboundchannel2__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_outboundchannel2__c es 11501. Lo que supone un 100.0%
Out[851]:
['msf_attribute_1__c',
 'msf_attribute_2__c',
 'msf_attribute_3__c',
 'msf_attribute_4__c',
 'msf_attribute_5__c',
 'msf_objectivepublic__c',
 'msf_outboundchannel2__c']
In [852]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[852]:
# Tot % Tot
11501 100.0
msf_outboundchannel2__c
Exite un 100% de vacios. Por lo que se descarta la variable.
Analsis de distribución por variables
-> msf_ownby__c: Variable Char
In [853]:
# Vamos a realizar analisis por cada variable
var = "msf_ownby__c"
In [854]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_ownby__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_ownby__c es 133. Lo que supone un 1.1564211807668898%
In [855]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[855]:
# Tot % Tot
Captación 8713 75.758630
Fidelización 1744 15.163899
Digital 474 4.121381
Desconocido 185 1.608556
Colaboraciones Estratégicas 157 1.365099
133 1.156421
Otros 95 0.826015
msf_ownby__c
Exite un 1.15% de vacios. Existe una alta concentración.
Analsis de distribución por variables
-> msf_previousstepchannel__c: Variable Char
In [856]:
# Vamos a realizar analisis por cada variable
var = "msf_previousstepchannel__c"
In [857]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_previousstepchannel__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_previousstepchannel__c es 11187. Lo que supone un 97.26980262585863%
Out[857]:
['msf_attribute_1__c',
 'msf_attribute_2__c',
 'msf_attribute_3__c',
 'msf_attribute_4__c',
 'msf_attribute_5__c',
 'msf_objectivepublic__c',
 'msf_outboundchannel2__c',
 'msf_previousstepchannel__c']
In [858]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[858]:
# Tot % Tot
11187 97.269803
Lead online 166 1.443353
SMS TV 43 0.373881
TLMK 42 0.365186
One to One 31 0.269542
Email 14 0.121729
SMS Opis 10 0.086949
SMS Push 5 0.043474
SMS otros 3 0.026085
msf_previousstepchannel__c
Exite un 97% de vacios. Por lo que se descarta la variable.
Analsis de distribución por variables
-> msf_promoterindividual__c: Variable Char
In [859]:
# Vamos a realizar analisis por cada variable
var = "msf_promoterindividual__c"
In [860]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_promoterindividual__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_promoterindividual__c es 7475. Lo que supone un 64.99434831753761%
Out[860]:
['msf_attribute_1__c',
 'msf_attribute_2__c',
 'msf_attribute_3__c',
 'msf_attribute_4__c',
 'msf_attribute_5__c',
 'msf_objectivepublic__c',
 'msf_outboundchannel2__c',
 'msf_previousstepchannel__c',
 'msf_promoterindividual__c']
In [861]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[861]:
# Tot % Tot
7475 64.994348
0033Y00002us2UrQAI 28 0.243457
0033Y00002up1OsQAI 23 0.199983
0033Y00003LdoAXQAZ 21 0.182593
0033Y00002zXqidQAC 16 0.139118
... ... ...
0033Y00003Oy0wlQAB 1 0.008695
0033Y00002unZbrQAE 1 0.008695
0033Y00002uokfgQAA 1 0.008695
0033Y00002up3jeQAA 1 0.008695
0033Y00003j0QGYQA2 1 0.008695

3410 rows × 2 columns

msf_promoterindividual__c
Exite un 64% de vacios. Por lo que se descarta la variable.
Analsis de distribución por variables
-> msf_provider__c: Variable Char
In [862]:
# Vamos a realizar analisis por cada variable
var = "msf_provider__c"
In [863]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_provider__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_provider__c es 7279. Lo que supone un 63.29014868272325%
Out[863]:
['msf_attribute_1__c',
 'msf_attribute_2__c',
 'msf_attribute_3__c',
 'msf_attribute_4__c',
 'msf_attribute_5__c',
 'msf_objectivepublic__c',
 'msf_outboundchannel2__c',
 'msf_previousstepchannel__c',
 'msf_promoterindividual__c',
 'msf_provider__c']
In [864]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[864]:
# Tot % Tot
7279 63.290149
Betternow 3310 28.780106
Datem 355 3.086688
Taskphone 231 2.008521
Desconocido 61 0.530389
Consolidar 46 0.399965
T2O 43 0.373881
Twisters 35 0.304321
Taskforce 33 0.286932
Entropia 14 0.121729
Prosocial 12 0.104339
Bodasnet 11 0.095644
Sitel 11 0.095644
Google Ads 11 0.095644
Zankyou 10 0.086949
FISL 7 0.060864
Paypal 6 0.052169
Fundraisingco 6 0.052169
Facebook 5 0.043474
INEK 4 0.034780
Centrocom 2 0.017390
Vodafone 1 0.008695
Worldcoo 1 0.008695
Movistar 1 0.008695
DGTL 1 0.008695
Pluscontacto 1 0.008695
Inneria 1 0.008695
Testamenta 1 0.008695
Iberian 1 0.008695
Busquets 1 0.008695
msf_provider__c
Exite un 63% de vacios. Por lo que se descarta la variable
Analsis de distribución por variables
-> msf_segment__c: Variable Char
In [865]:
# Vamos a realizar analisis por cada variable
var = "msf_segment__c"
In [866]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_segment__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_segment__c es 110. Lo que supone un 0.9564385705590819%
In [867]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[867]:
# Tot % Tot
Frio individuos 5635 48.995740
Frío individuos 3329 28.945309
Mass donors 1183 10.286062
Mid donors 363 3.156247
Leads 280 2.434571
Organizaciones 145 1.260760
Mid+ donors 142 1.234675
Desconocido 125 1.086862
110 0.956439
Testamentarios 87 0.756456
One to One 66 0.573863
Asociados España 10 0.086949
Asociados 9 0.078254
Celebraciones 7 0.060864
Otros 6 0.052169
Terreno 4 0.034780
msf_segment__c
Exite un 1% de vacios. Existe una alta concentración.
Analsis de distribución por variables
-> recordtypeid: Variable Char
In [868]:
# Vamos a realizar analisis por cada variable
var = "recordtypeid"
In [869]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable recordtypeid es 0. Lo que supone un 0.0%
El nº de vacios para la variable recordtypeid es 0. Lo que supone un 0.0%
In [870]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[870]:
# Tot % Tot
0120O000000kNMGQA2 11459 99.634814
0123Y000000ZVFyQAO 42 0.365186
recordtypeid
No existen vacios. Existe una alta concentración. Por lo que se descarta la variable
Analsis de distribución por variables
-> status: Variable Char
In [871]:
# Vamos a realizar analisis por cada variable
var = "status"
In [872]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable status es 0. Lo que supone un 0.0%
El nº de vacios para la variable status es 0. Lo que supone un 0.0%
In [873]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[873]:
# Tot % Tot
Completed 8780 76.341188
In Progress 1494 12.990175
Created 1207 10.494740
Canceled 20 0.173898
status
No existen vacios. Existe una alta concentración. Por lo que se descarta la variable
Analsis de distribución por variables
-> ownerid: Variable Char
In [874]:
# Vamos a realizar analisis por cada variable
var = "ownerid"
In [875]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable ownerid es 0. Lo que supone un 0.0%
El nº de vacios para la variable ownerid es 0. Lo que supone un 0.0%
In [876]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[876]:
# Tot % Tot
0050O000009jTv8QAE 6257 54.403965
0050O0000092PPIQA2 2080 18.085384
0050O000009jOwLQAU 1077 9.364403
0053Y00000AHjdNQAT 396 3.443179
0053Y00000AHjd3QAD 395 3.434484
0050O0000073qRgQAI 285 2.478045
0053Y00000AHKLsQAP 214 1.860708
0053Y000009bDUuQAM 206 1.791149
0053Y0000096s8QQAQ 144 1.252065
0053Y00000A6ntvQAB 100 0.869490
0053Y00000AKTiDQAX 57 0.495609
0053Y00000AVogPQAT 56 0.486914
0050O000009jVBvQAM 49 0.426050
0053Y0000096yrMQAQ 42 0.365186
0053Y00000A6arDQAR 26 0.226067
0050O000007DgJhQAK 25 0.217372
0053Y000008IZePQAW 15 0.130423
0053Y00000A6YtHQAV 13 0.113034
0053Y0000096yKrQAI 13 0.113034
0053Y0000096yr7QAA 10 0.086949
0053Y00000AHjdhQAD 9 0.078254
0053Y00000AHXaHQAX 6 0.052169
0053Y00000AHQCYQA5 5 0.043474
0050O0000073qbgQAA 4 0.034780
0050O000007DgJcQAK 4 0.034780
0050O0000073qbWQAQ 3 0.026085
0053Y00000AHQCbQAP 3 0.026085
0053Y00000A6b9CQAR 3 0.026085
0053Y00000A6cgCQAR 2 0.017390
0053Y00000BR4oJQAT 1 0.008695
0053Y00000AHjceQAD 1 0.008695
ownerid
No existen vacios. Existe una alta concentración por lo que se descarta la variable.
Analsis de distribución por variables
-> msf_thematic__c: Variable Char
In [877]:
# Vamos a realizar analisis por cada variable
var = "msf_thematic__c"
In [878]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_thematic__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_thematic__c es 111. Lo que supone un 0.9651334666550734%
In [879]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[879]:
# Tot % Tot
90 6128 53.282323
34 801 6.964612
81 791 6.877663
00 519 4.512651
33 348 3.025824
74 274 2.382402
04 212 1.843318
28 149 1.295540
10 142 1.234675
06 133 1.156421
66 122 1.060777
05 121 1.052082
07 113 0.982523
111 0.965133
65 87 0.756456
08 87 0.756456
39 85 0.739066
03 83 0.721676
71 81 0.704287
52 77 0.669507
31 76 0.660812
99 64 0.556473
53 52 0.452135
85 52 0.452135
70 51 0.443440
50 48 0.417355
02 47 0.408660
11 43 0.373881
36 41 0.356491
37 34 0.295626
18 31 0.269542
42 30 0.260847
12 30 0.260847
80 26 0.226067
60 26 0.226067
86 23 0.199983
54 22 0.191288
72 19 0.165203
91 18 0.156508
43 18 0.156508
Asamblea General 17 0.147813
40 16 0.139118
MSF España 15 0.130423
64 14 0.121729
69 14 0.121729
32 12 0.104339
59 12 0.104339
17 11 0.095644
83 - Afganistán 11 0.095644
57 10 0.086949
67 9 0.078254
82-Tigray 9 0.078254
30 8 0.069559
63 8 0.069559
68 7 0.060864
27 7 0.060864
49 7 0.060864
61 6 0.052169
Vida asociativa 6 0.052169
38 6 0.052169
76 6 0.052169
55 6 0.052169
25 5 0.043474
77 5 0.043474
13 5 0.043474
15 4 0.034780
Gobernanza 4 0.034780
78 4 0.034780
87 4 0.034780
84 3 0.026085
45 3 0.026085
46 3 0.026085
22 3 0.026085
41 2 0.017390
51 2 0.017390
24 2 0.017390
62 2 0.017390
20 2 0.017390
48 2 0.017390
56 2 0.017390
23 2 0.017390
88 1 0.008695
89 1 0.008695
21 1 0.008695
58 1 0.008695
XX 1 0.008695
35 1 0.008695
29 1 0.008695
75 1 0.008695
44 1 0.008695
16 1 0.008695
msf_thematic__c
Existe un 1% de vacíos. Existe una alta concentración por lo que se descarta la variable.
In [ ]:
 

5. Tabla Tareas¶

In [880]:
# Vamos a analizar la tabla Campañas
df = df_tareas
In [881]:
# Se crea una lista por ahora vacia, en la que se irán añadiendo las variables que se van a eliminar del dataset por motivos varios: no utilidad, gran volumen de nulos, ...
col_to_delete_tareas=list()
Analsis de distribución por variables
-> msf_Objective__c: Variable Char
In [882]:
# Vamos a realizar analisis por cada variable
var = "msf_Objective__c"
In [883]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_Objective__c es 0. Lo que supone un 0.0%
El nº de vacios para la variable msf_Objective__c es 0. Lo que supone un 0.0%
In [884]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[884]:
# Tot % Tot
Petición económica-Upgrade Socio 2611895 99.995827
Petición económica-Conversión Prospecto 107 0.004096
Gestión administrativa 1 0.000038
Petición económica-Resto 1 0.000038
msf_Objective__c.
Exite un 0% de vacios. Existe una alta concentracion por lo que se descarta la variable.
Analsis de distribución por variables
-> msf_CloseType__c: Variable Char
In [885]:
# Vamos a realizar analisis por cada variable
var = "msf_CloseType__c"
In [886]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_CloseType__c es 62202. Lo que supone un 2.381389921301805%
El nº de vacios para la variable msf_CloseType__c es 0. Lo que supone un 0.0%
In [887]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[887]:
# Tot % Tot
No util 958125 37.576447
Negativo 840737 32.972639
Positivo 574342 22.524965
Descargada 91922 3.605064
No útil 55934 2.193661
Potencial 28742 1.127225
msf_CloseType__c.
Exite un 0% de vacios. Existe una buena distribucion.
Analsis de distribución por variables
-> ActivityDate: Variable Char
In [888]:
# Vamos a realizar analisis por cada variable
var = "ActivityDate"
In [889]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable ActivityDate es 0. Lo que supone un 0.0%
El nº de vacios para la variable ActivityDate es 0. Lo que supone un 0.0%
In [890]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[890]:
# Tot % Tot
2016-10-28 44842 1.716766
2015-05-17 32781 1.255013
2023-05-26 32705 1.252104
2013-10-11 23392 0.895558
2018-09-30 22995 0.880359
... ... ...
2015-05-16 1 0.000038
2014-10-26 1 0.000038
2013-10-13 1 0.000038
2022-07-02 1 0.000038
2023-07-02 1 0.000038

2748 rows × 2 columns

ActivityDate.
Exite un 0% de vacios. Existen una gran gama de valores posibles con mucha distribución entre ellos.
Analsis de distribución por variables
-> msf_Channel__c: Variable Char
In [891]:
# Vamos a realizar analisis por cada variable
var = "msf_Channel__c"
In [892]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_Channel__c es 1. Lo que supone un 3.828478057460861e-05%
El nº de vacios para la variable msf_Channel__c es 0. Lo que supone un 0.0%
In [893]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[893]:
# Tot % Tot
Llamada 2513077 96.212638
E-mail 98920 3.787132
Interno 2 0.000077
Mensajería Instantánea 2 0.000077
Fichero Informático 1 0.000038
Correo Postal 1 0.000038
msf_Channel__c.
Exite un 0% de vacios. Existe una alta concentracion por lo que se descarta la variable.
Analsis de distribución por variables
-> msf_Campaign__c: Variable Char
In [894]:
# Vamos a realizar analisis por cada variable
var = "msf_Campaign__c"
In [895]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_Campaign__c es 210116. Lo que supone un 8.044244955214463%
El nº de vacios para la variable msf_Campaign__c es 0. Lo que supone un 0.0%
In [896]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[896]:
# Tot % Tot
7013Y000001mqtMQAQ 1176702 48.990711
7013Y000001vXGdQAM 224728 9.356306
7013Y000001n865QAA 138421 5.763008
7013Y000001vXGiQAM 110245 4.589931
7013Y000001n860QAA 104716 4.359737
7013Y000001DrxyQAC 96915 4.034951
7013Y000001vZqHQAU 88236 3.673610
7013Y000001DrxoQAC 67535 2.811746
7013Y000001mrgZQAQ 62087 2.584925
7013Y000001myedQAA 51595 2.148102
7013Y000001mqtuQAA 51081 2.126702
7013Y000001mrgeQAA 36976 1.539456
7013Y000001mrgcQAA 34374 1.431124
7013Y000001myefQAA 27801 1.157464
7013Y000001najdQAA 23703 0.986849
7013Y000001vZoBQAU 23536 0.979896
7013Y000001myeaQAA 20745 0.863696
7013Y000001n4QNQAY 10643 0.443110
7013Y000000kQ7rQAE 9863 0.410635
7013Y000001njxrQAA 7806 0.324994
7013Y000001mrgaQAA 5163 0.214956
7013Y000001mrjZQAQ 3894 0.162122
7013Y000001mrjVQAQ 3372 0.140390
7013Y000001mrgXQAQ 2806 0.116825
7013Y000001myebQAA 2299 0.095716
7013Y000001n4QWQAY 2125 0.088472
7013Y000001mqt6QAA 2117 0.088139
7013Y000001mrgbQAA 1830 0.076190
7013Y000001mrgdQAA 1488 0.061951
7013Y000001mrjXQAQ 1392 0.057954
7013Y000001mrgYQAQ 1298 0.054041
7013Y000001mrgfQAA 836 0.034806
7013Y000001mrjlQAA 824 0.034306
7013Y000001vZoGQAU 814 0.033890
7013Y000001myecQAA 802 0.033390
7013Y000001myeYQAQ 655 0.027270
7013Y000001myeeQAA 367 0.015280
7013Y000001myeZQAQ 313 0.013031
7013Y000001mrjkQAA 302 0.012573
7013Y000001mrjnQAA 252 0.010492
7013Y000001myegQAA 241 0.010034
7013Y000001mrjmQAA 216 0.008993
7013Y000001mrjoQAA 134 0.005579
7013Y000001vCMxQAM 105 0.004372
7013Y000001mrjfQAA 100 0.004163
7013Y000001mN9IQAU 92 0.003830
7013Y000001mN9DQAU 92 0.003830
7013Y000001mrjpQAA 78 0.003247
7013Y000001mqrjQAA 73 0.003039
7013Y000001mqt7QAA 63 0.002623
7013Y000001mrjqQAA 22 0.000916
7013Y000001mquNQAQ 11 0.000458
7013Y000001mrZeQAI 2 0.000083
7013Y000001mrjWQAQ 1 0.000042
7013Y000001mN98QAE 1 0.000042
msf_Campaign__c.
Exite un 0% de vacios y un 8% de nulos. Existe una alta concentracion.
Analsis de distribución por variables
-> msf_StartDate__c: Variable Char
In [897]:
# Vamos a realizar analisis por cada variable
var = "msf_StartDate__c"
In [898]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_StartDate__c es 1526897. Lo que supone un 58.45691660502818%
El nº de vacios para la variable msf_StartDate__c es 0. Lo que supone un 0.0%
Out[898]:
['msf_attribute_1__c',
 'msf_attribute_2__c',
 'msf_attribute_3__c',
 'msf_attribute_4__c',
 'msf_attribute_5__c',
 'msf_objectivepublic__c',
 'msf_outboundchannel2__c',
 'msf_previousstepchannel__c',
 'msf_promoterindividual__c',
 'msf_provider__c',
 'msf_StartDate__c']
In [899]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[899]:
# Tot % Tot
2020-10-08 91649 8.446080
2023-05-26 47000 4.331370
2022-12-05 42875 3.951223
2022-10-19 40000 3.686272
2020-10-05 29023 2.674667
2020-11-27 28449 2.621769
2021-06-01 27102 2.497634
2020-11-02 25697 2.368154
2023-03-03 23593 2.174256
2023-02-03 23504 2.166054
2023-03-29 23500 2.165685
2023-06-27 23500 2.165685
2023-04-28 23450 2.161077
2022-10-18 23107 2.129467
2021-11-09 22884 2.108916
2021-02-01 22001 2.027542
2022-11-07 21648 1.995011
2022-04-06 21600 1.990587
2022-10-06 21600 1.990587
2022-06-08 21600 1.990587
2022-09-07 21600 1.990587
2022-01-10 21600 1.990587
2022-08-03 21600 1.990587
2022-07-07 21600 1.990587
2022-03-10 21600 1.990587
2022-05-10 21600 1.990587
2022-02-10 21473 1.978883
2021-09-07 19968 1.840187
2021-07-01 19846 1.828944
2021-03-01 18965 1.747754
2021-12-02 17720 1.633019
2021-10-05 17386 1.602238
2023-01-09 16300 1.502156
2021-09-28 13785 1.270382
2021-08-03 13312 1.226791
2020-11-06 12765 1.176382
2021-10-04 11672 1.075654
2021-05-03 11582 1.067360
2021-09-08 11032 1.016674
2023-04-19 10375 0.956127
2021-04-07 9374 0.863878
2021-04-06 9311 0.858072
2021-10-28 9205 0.848303
2023-01-04 8967 0.826370
2021-10-26 7806 0.719376
2023-02-15 6075 0.559853
2023-07-07 5932 0.546674
2022-11-25 5177 0.477096
2020-12-29 4938 0.455070
2022-09-08 4758 0.438482
2022-12-28 4581 0.422170
2023-02-01 4516 0.416180
2023-07-05 4314 0.397564
2021-05-04 3851 0.354896
2023-06-28 3834 0.353329
2022-12-24 3270 0.301353
2022-11-09 3229 0.297574
2021-01-15 2900 0.267255
2023-02-22 2812 0.259145
2023-01-17 2724 0.251035
2022-12-08 2682 0.247165
2023-01-11 2632 0.242557
2022-11-30 2443 0.225139
2023-03-09 2046 0.188553
2023-05-03 2024 0.186525
2023-03-14 1818 0.167541
2023-04-26 1268 0.116855
2022-09-22 1120 0.103216
2023-01-26 1067 0.098331
2023-02-09 1042 0.096027
2023-07-04 1000 0.092157
2023-05-10 992 0.091420
2023-06-30 992 0.091420
2023-06-06 962 0.088655
2022-12-14 903 0.083218
2022-07-27 814 0.075016
2023-03-02 779 0.071790
2020-12-30 604 0.055663
2022-09-09 365 0.033637
2023-03-16 317 0.029214
2022-11-28 192 0.017694
2020-12-17 184 0.016957
2023-06-14 176 0.016220
2023-05-18 161 0.014837
2023-06-21 101 0.009308
2022-12-22 97 0.008939
2022-11-19 95 0.008755
2023-05-16 83 0.007649
2023-01-12 83 0.007649
2023-06-16 70 0.006451
2023-02-10 68 0.006267
2022-10-27 67 0.006175
2022-12-01 64 0.005898
2023-01-25 56 0.005161
2022-11-10 52 0.004792
2023-04-27 50 0.004608
2022-10-21 49 0.004516
2023-01-19 43 0.003963
2022-10-12 41 0.003778
2022-10-05 41 0.003778
2022-12-30 35 0.003225
2022-11-02 30 0.002765
2022-09-28 28 0.002580
2022-12-29 17 0.001567
2022-12-25 15 0.001382
2023-02-25 11 0.001014
2020-11-20 10 0.000922
2023-02-24 9 0.000829
2022-12-10 9 0.000829
2022-11-17 6 0.000553
2023-05-04 6 0.000553
2023-02-23 6 0.000553
2023-01-31 5 0.000461
2023-03-01 5 0.000461
2020-09-30 5 0.000461
2023-05-17 4 0.000369
2023-03-11 4 0.000369
2023-01-13 4 0.000369
2022-11-03 4 0.000369
2023-02-16 3 0.000276
2023-03-07 3 0.000276
2023-02-26 3 0.000276
2023-05-07 3 0.000276
2023-03-10 3 0.000276
2020-11-05 3 0.000276
2020-10-30 3 0.000276
2023-01-27 3 0.000276
2023-01-07 3 0.000276
2020-10-16 3 0.000276
2023-02-21 2 0.000184
2023-02-02 2 0.000184
2023-04-20 2 0.000184
2023-05-12 2 0.000184
2023-02-12 2 0.000184
2022-12-15 2 0.000184
2022-12-17 2 0.000184
2023-07-03 2 0.000184
2020-09-22 2 0.000184
2022-12-03 2 0.000184
2022-12-06 2 0.000184
2023-01-14 2 0.000184
2022-07-13 2 0.000184
2023-05-30 2 0.000184
2021-04-28 2 0.000184
2023-05-08 1 0.000092
2023-04-25 1 0.000092
2023-01-21 1 0.000092
2023-04-30 1 0.000092
2023-05-28 1 0.000092
2023-05-02 1 0.000092
2023-04-24 1 0.000092
2023-05-13 1 0.000092
2021-04-01 1 0.000092
2023-05-19 1 0.000092
2022-07-06 1 0.000092
2022-07-05 1 0.000092
2023-05-11 1 0.000092
2022-06-22 1 0.000092
2023-05-20 1 0.000092
2023-04-21 1 0.000092
2021-02-17 1 0.000092
2023-01-16 1 0.000092
2021-03-08 1 0.000092
2023-02-18 1 0.000092
2021-11-04 1 0.000092
2021-06-15 1 0.000092
2023-01-06 1 0.000092
2021-11-05 1 0.000092
2022-03-02 1 0.000092
2021-03-24 1 0.000092
2021-05-07 1 0.000092
2021-11-11 1 0.000092
2021-11-12 1 0.000092
2021-09-24 1 0.000092
2020-09-23 1 0.000092
2021-01-07 1 0.000092
2020-11-03 1 0.000092
2020-11-25 1 0.000092
2021-02-12 1 0.000092
2020-10-28 1 0.000092
2023-01-10 1 0.000092
2020-09-24 1 0.000092
2023-01-20 1 0.000092
2022-11-18 1 0.000092
2022-01-05 1 0.000092
2021-09-01 1 0.000092
2022-12-16 1 0.000092
2023-02-11 1 0.000092
2023-02-28 1 0.000092
2021-08-24 1 0.000092
2021-06-17 1 0.000092
2022-12-09 1 0.000092
2023-04-12 1 0.000092
2023-01-05 1 0.000092
2023-01-01 1 0.000092
2022-11-22 1 0.000092
2023-01-15 1 0.000092
2023-07-02 1 0.000092

msf_StartDate__c.
Exite un 58% de vacios. Por lo que se descarta la variable

Analsis de distribución por variables
-> Status: Variable Char
In [900]:
# Vamos a realizar analisis por cada variable
var = "Status"
In [901]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable Status es 0. Lo que supone un 0.0%
El nº de vacios para la variable Status es 0. Lo que supone un 0.0%
In [902]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[902]:
# Tot % Tot
Realizada 2493233 95.452878
Cancelada 78406 3.001757
En curso 40358 1.545097
Pendiente 7 0.000268
Status.
Exite un 0% de vacios. Existe una alta concentracion por lo que se descarta la variable.
Analsis de distribución por variables
-> WhoId: Variable Char
In [903]:
# Vamos a realizar analisis por cada variable
var = "WhoId"
In [904]:
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable WhoId es 0. Lo que supone un 0.0%
El nº de vacios para la variable WhoId es 0. Lo que supone un 0.0%
In [905]:
# Analizamos posibles valores de la variable
freq_variables(df,var)
Out[905]:
# Tot % Tot
0033Y00002uNnhXQAS 20 0.000766
0033Y00002uNx9eQAC 19 0.000727
0033Y00002unVk3QAE 18 0.000689
0033Y00002upRCVQA2 18 0.000689
0033Y00002uNtqnQAC 18 0.000689
... ... ...
0033Y00002uoIGUQA2 1 0.000038
0033Y00002uo6BbQAI 1 0.000038
0033Y00002unwzPQAQ 1 0.000038
0033Y00002uo2o9QAA 1 0.000038
0033Y00003CCkqXQAT 1 0.000038

593604 rows × 2 columns

WhoId.
Exite un 0% de vacios.
In [ ]:
 
In [ ]:
 
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